Please write a perfect abstract for this manuscript that is appropriate for publishing in high-impact factor journals such as The Lancet, Nature, and JAMA:
"1.	Background
Testis-specific gene antigen 10 (TSGA10) encodes a protein expressed in proliferating tissues, including the testis, differentiating fetal tissues and several primary tumors (1–3). Studies have demonstrated that TSGA10 plays a role in mitotic division during carcinogenesis, neurogenesis, embryogenesis, and spermatogenesis (1,4). TSGA10 produces two transcript variants through alternative splicing of exon 16. The long transcript encodes an 82 kDa protein (accession number AAH28366). Posttranslational modification and cleavage divide the full-length TSGA10 protein into two components: an N-terminal 27 kDa fragment located in the fibrous sheath of sperm flagella and a C-terminal 55 kDa fragment (5). The C-terminal 55 kDa fragment of TSGA10 interacts with the PASB domain of hypoxia-inducible factor-1α (HIF-1α), preventing its dimerization and inhibiting recruitment of the coactivator P300, leading to HIF-1α inactivation (4). As HIF-1α is a critical transcriptional regulator of genes involved in cellular metabolism, growth, viability, angiogenesis, autophagy, and apoptosis (6), TSGA10 may affect these processes, particularly autophagy and apoptosis. TSGA10 is also associated with acephalic spermatozoa syndrome (7,8).
Autophagy involves the sequestration of cytoplasmic material and organelles within a double membrane-bound vesicle termed an autophagosome, which then fuses with lysosomes, leading to the degradation of the contents. This evolutionarily conserved catabolic process occurs in all eukaryotic cells and is tightly regulated by various signals and factors. By breaking down cellular components, autophagy promotes cell survival during starvation. Dysregulated autophagy has been implicated in numerous human diseases and cancers (9). While conferring certain advantages, autophagy can promote cancer cell survival and proliferation in some contexts (10,11). Furthermore, in specific cancer cells, autophagy can trigger cell death through a process known as autophagy-dependent cell death (12–14).
Autophagy and apoptosis represent distinct mechanisms of cellular self-degradation that regulate cell survival versus death (15). Apoptosis is programmed cell death characterized by cytoplasmic shrinkage, chromatin condensation, and nuclear fragmentation. Physiological or pathological stimuli can induce apoptosis and have been proposed as potential cancer therapeutic targets (16). Although TSGA10 expression is widespread in normal tissues, it is dysregulated in autoimmune diseases and various cancers (3,17). Some studies have suggested that TSGA10 may function as a cancer-testis antigen, while others indicate that it may act as a tumor suppressor (4,18,19).
There is evidence that TSGA10 may inhibit vimentin, an intermediate filament protein, although the mechanism is unclear (20,21). Vimentin forms a complex with Beclin-1 and 14-3-3 proteins that impedes autophagosome formation, thereby inhibiting autophagy (22). This suggests that TSGA10 may indirectly modulate autophagy through effects on vimentin and Beclin-1. Our previous study showed that TSGA10 leads to HIF-1α inactivation, which could indirectly inhibit BCL-2 (4), a key regulator of apoptosis and autophagy targeted by many cancer therapies (23). However, the role of TSGA10 in modulating autophagy and apoptosis remains unknown. Therefore, we investigated the effect of TSGA10 overexpression on autophagy and apoptosis in HeLa cells to elucidate the involvement of TSGA10 in these processes, particularly within the cancer context.
2.	Material and methods 
2.1.	Chemicals and Reagents
The following primary antibodies were acquired from Santa Cruz Biotechnology: goat anti-TSGA10, mouse anti-BCL-2, mouse anti-BAX, goat anti-caspase-3, goat anti-LC3I, and goat anti-LC3II. Additional reagents utilized were cisplatin and NH4Cl from Sigma‒Aldrich, rapamycin from InvivoGen, G418 from Alexis Biochemicals, acridine orange and Annexin V/PI assay kits from Abcam and Sigma‒Aldrich, Lipofectamine 2000, pcDNA3.1 vector from Invitrogen, trypsin-EDTA from Invitrogen, and T4 DNA ligase from Takara Bio. All other chemical reagents were purchased from Sigma‒Aldrich.
2.2.	Plasmid Construction
To generate a TSGA10 expression vector, the following primers were used for PCR amplification: 5’-AGGATCCGCCACCATGATGCGAAGTAGGTC-3’ containing a BamHI restriction site (underlined) and 5’-AGGAATTCGTCAGAAATCTCTGTAGC-3’ containing an EcoRI site (underlined). These primers targeted the coding sequence of the TSGA10 gene (Accession No. AF254756.1). Approval for using cell lines was obtained from the research ethics committee at Kermanshah University of Medical Sciences. PCR was performed using the above primers and 10 μg of human testis cDNA under the following conditions: initial denaturation at 94°C for 1 min, followed by 35 cycles of denaturation at 94°C for 1 min, annealing at 60°C for 90 sec, extension at 72°C for 1 min, and a final extension at 72°C for 10 min (4). This generated a 2115 bp PCR product flanked by BamHI and EcoRI sites. The amplified PCR product was double digested with BamHI and EcoRI, purified, and ligated into the BamHI/EcoRI-linearized pcDNA3.1 vector using T4 DNA ligase. The ligation mixture was transformed into DH5α competent cells. Recombinant clones were verified by restriction analysis, PCR screening, and DNA sequencing to validate the clone reading frame.
2.3.	Cell culture and transfection
HeLa cells obtained from the Pasteur Institute were utilized as a tumor cell model, while primary human skin fibroblasts were isolated as previously described and used as normal control cells. Cell line authentication was performed by STR profiling (24). Cells were cultured in DMEM/F12 supplemented with 10% fetal bovine serum, 100 μg/ml streptomycin, and 100 U/ml penicillin at 37°C in a humidified 5% CO2 incubator. For transfection, cells were seeded into 24-well plates at a density of 1-2 × 105 cells/well. At ~85-90% confluence, cells were transfected using Lipofectamine 2000 with different amounts of plasmid DNA (0.5-2 μg). Transfected cells were selected in medium containing 1 mg/ml G418 for four weeks to generate stable polyclonal populations. Individual G418-resistant colonies were isolated and expanded to generate clonal cell strains. TSGA10-transfected cells (H.pc-TSGA10) and empty vector control cells (H.pc) were maintained separately in G418 (200 μg/ml) to ensure stable expression. TSGA10 mRNA and protein expression were analyzed in resistant clones by real-time PCR, immunoblotting, and immunocytochemistry.
2.4.	Cell viability assay
HeLa cells were seeded at 5×103 cells/well in 96-well plates in 10% FBS medium and cultured for 48 and 72 hrs. For hypoxia treatment, plates were incubated in a hypoxia chamber equilibrated to 1% O2 using a nitrogen gas purge to displace oxygen. Parallel normoxic control plates were maintained in a standard cell culture incubator. Cell viability was measured by adding 20 μl/well of 5 mg/ml MTT reagent and incubating for 4 hrs, followed by dissolution of formazan crystals in 200 μl DMSO. Absorbance was read at 570 nm using a microplate spectrophotometer, with background subtraction at 630 nm. Experiments were performed in sextuplicate.
2.5. Real-time PCR
H.pc and H.pc-TSGA10 cells were cultured under normoxic or hypoxic conditions, with or without 100 nM rapamycin treatment, for 18 hrs to induce autophagy. Total RNA was extracted using TRIzol reagent and reverse transcribed to cDNA. Target gene expression was analyzed by real-time PCR using Takara SYBR Green master mix and the Rotor-Gene 6000 system with the following cycling conditions: 95°C for 15 min, then 40 cycles of 95°C for 15 sec, 60°C for 15 sec, and 72°C for 20 sec, followed by melting curve analysis from 60-95°C. Relative expression analysis was conducted using the REST software tool.
2.6. Western blot
H.pc and H.pc-TSGA10 cells were cultured under normoxic or hypoxic conditions and lysed in Tris buffer (0.075 M, pH 7.6) containing 9 M urea. The Bradford assay estimated the protein concentration. SDS‒PAGE resolved proteins (40-50 μg per sample) were transferred to PVDF membranes under reducing conditions. Membranes were blocked in 5% nonfat milk in PBS for 2 hrs at 25°C and probed with primary antibodies against TSGA10, BAX, BCL-2, caspase-3, LC3I, and LC3II, followed by HRP-conjugated secondary antibodies. Protein bands were visualized using chemiluminescence detection. Band intensities were quantified by densitometry, normalized to β-actin expression, and analyzed using TotalLab software.
2.7. Acidic Vesicle Detection
To monitor acidic vesicle formation, H.pc and H.pc-TSGA10 cells were stained with acridine orange (1 μg/ml) for 30 min under normoxic or hypoxic conditions, with or without rapamycin treatment. Cells were fixed in 4% paraformaldehyde and examined by fluorescence microscopy. The intensity of red fluorescence reflects the abundance of acidic vesicles and the degree of autophagy.
2.8. Molecular modeling and docking
Due to a lack of crystallographic data, homology modeling (HM) was utilized to generate a 3D structural model of TSGA10 (Q9BZW7). The TSGA10 sequence was obtained from UniProt and used to query the Protein Data Bank on BLAST to identify suitable HM templates based on high sequence identity. The templates selected were 3S4R (vimentin), 3CBU (glutathione S-transferase), 4IKM (CARD8), 1OBA (phage CP-1), 4E61 (Bim1p), and 2IXU (Cpl-1). Using multiple templates enhances model accuracy for proteins with low identity (<40%) (25). Models were constructed with MODELLER 9.8 (26) and evaluated using PROCHECK (27) to select the best model with the lowest probability density function (PDF) and least violations. Loop refinement was performed using Modeler's ab initio loop prediction.
The HADDOCK web server (27) was utilized to model TSGA10-BCL2 interactions using docking guided by experimental data. The BCL2 structure was obtained from the PDB (1G5M). Docking was performed in three stages: rigid body energy minimization, semiflexible simulated annealing, and flexible explicit solvent refinement. Residues designated as “active” (experimentally implicated in binding) and “passive” (neighboring residues) were used to guide docking. The lowest energy model was selected for analysis.
2.9. Statistical analysis
Data are presented as the mean ± SD. Statistical comparisons between groups were conducted by appropriate methods such as t test, one-way or two-way ANOVA followed by post hoc tests (Tukey's test) using GraphPad Prism 8 software. P < 0.05 was considered statistically significant.
3.	Results
3.1.	TSGA10 expression
Stably transfected HeLa clones overexpressing TSGA10 were generated and analyzed for TSGA10 mRNA levels by quantitative PCR. Relative to control HeLa-pcDNA3.1 cells, TSGA10 mRNA expression was increased by 1.5 ± 0.34-fold in clone 1, 3.4 ± 0.2-fold in clone 2, and 5.8 ± 0.38-fold in clone 3. Human fibroblasts exhibited 3.93 ± 0.45-fold higher TSGA10 mRNA than control HeLa cells (Figure 1A). Densitometry confirmed that clone 3 had the highest TSGA10 protein expression among the clones (Figure 1B). Immunocytochemical analysis further validated TSGA10 overexpression in the transfected HeLa clones (Figure 1C).
Figure 1. Analysis of autophagy- and apoptosis-related gene and protein expression in H.pc and H.pc-TSGA10 cells under normoxic and hypoxic conditions. (A) Quantitative PCR analysis demonstrated significantly increased TSGA10 mRNA levels in three H.pc-TSGA10 clones compared to wild-type H.pc cells and normal human fibroblasts. (B) Western blot analysis revealed markedly higher TSGA10 protein expression in H.pc-TSGA10 clones versus controls. (C) Immunocytochemical staining confirmed abundant TSGA10 protein in H.pc-TSGA10 but not wild-type H.pc cells. (D) Western blotting showed increased LC3A-II and LC3B-II levels, markers of autophagosome formation, in H.pc-TSGA10 cells and normal fibroblasts treated with the autolysosomal inhibitor NH4Cl under hypoxia compared to normoxia. (F) Quantitative PCR analysis revealed decreased anti-apoptotic BCL-2 and increased pro-apoptotic BAX mRNA levels in H.pc-TSGA10 versus H.pc cells under hypoxia. (G, H) Western blots demonstrated reduced BCL-2 and elevated BAX protein expression in H.pc-TSGA10 cells under hypoxia. (I) The BAX/BCL-2 protein ratio significantly increased in H.pc-TSGA10 cells under hypoxia compared to normoxia and wild-type H.pc cells. Data represent the mean ± SD of 5-6 independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001 compared to controls.
3.2.	TSGA10 Overexpression Induces Autophagy in HeLa Cells
To investigate the role of TSGA10 in regulating autophagy, we first overexpressed TSGA10 in HeLa cells. We selected clone 3, which exhibited a 5.8-fold increase in TSGA10 mRNA, for further analysis. Immunoblotting revealed enhanced LC3II expression and LC3II/LC3I ratio (indicative of autophagosome accumulation) in H.pc-TSGA10 versus H.pc cells under both normoxic and hypoxic conditions, with or without the autolysosomal inhibitor NH4Cl (10 mM) (Figure 1F). To determine if TSGA10 upregulates autophagy-related genes, we performed real-time PCR analysis of Beclin1, BNIP3, NIX, ATG5, LC3I, and LC3II mRNA levels in H.pc-TSGA10 and control H.pc cells under normoxic and hypoxic conditions. We found that H.pc-TSGA10 cells exhibited significantly higher expression of all measured autophagy-related genes than H.pc cells (Figure 2). Furthermore, acridine orange staining and fluorescence microscopy revealed that H.pc-TSGA10 cells developed more acidic cytoplasmic compartments faster than H.pc cells under basal and rapamycin-treated conditions, reflecting greater autophagic flux (Figure 3).
Figure 2. Expression of autophagy-related genes in H.pc-TSGA10 cells compared to H.pc cells under normoxic and hypoxic conditions with or without rapamycin treatment. Quantitative real-time PCR and acridine orange staining were utilized to analyze autophagy marker mRNA and protein levels. Data are presented as the mean ± standard deviation from 5-6 independent experiments. Statistical significance relative to the control is indicated as *P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 3. Acridine orange analysis in H.pc-TSGA10 and H.pc cells with or without rapamycin treatment.
3.3.	TSGA10 Overexpression Stimulates Apoptosis in HeLa Cells
We analyzed the expression of pro-apoptotic BAX, anti-apoptotic BCL-2, and executor caspase-3 in H.pc-TSGA10 versus control cells to investigate whether TSGA10 overexpression induces apoptosis. Immunoblotting and real-time PCR demonstrated reduced BCL-2 expression and elevated BAX levels in H.pc-TSGA10 cells under normoxic and hypoxic conditions compared to controls (Figure 1H-I). The BAX/Bcl2 ratio, an indicator of apoptotic drive, was significantly higher in H.pc-TSGA10 cells. Under normoxia and hypoxia, cleaved caspase-3 was increased in H.pc-TSGA10 cells relative to H.pc cells, while procaspase-3 levels were reciprocally reduced, confirming greater caspase-3 activation (Figure 4). These results demonstrate that TSGA10 overexpression enhances apoptosis in HeLa cells.
Figure 4. Analysis of caspase-3 activation in H.pc-TSGA10 cells compared to parental H.pc cells following 48 hours of normoxic or hypoxic incubation. (A) Immunoblot analysis of procaspase-3 and cleaved (active) caspase-3 expression. (B, C) Densitometric quantitation of procaspase-3 and cleaved caspase-3 levels normalized to the β-actin loading control. Data represent the mean ± standard deviation of 4-5 independent experiments. Statistical significance relative to the control is indicated as *P < 0.05, **P < 0.01, and ***P < 0.001.
3.4.	TSGA10 Overexpression Reduces Cell Viability
We performed MTT assays to determine the impact of TSGA10 overexpression on HeLa cell viability. Under both normoxic and hypoxic conditions, TSGA10-overexpressing cells exhibited significantly decreased viability compared to control cells, possibly due to upregulated autophagy and apoptosis. Moreover, treatment with the autophagy inducer rapamycin or the apoptosis inducer cisplatin reduced viability at lower concentrations in H.pc-TSGA10 versus H.pc cells (Figure 5A-D). HeLa cells transfected with higher amounts of the TSGA10 expression vector showed markedly lower viability than cells transfected with an empty vector, further confirming the cytotoxic effects of TSGA10 (Figure 6A-D).
Figure 5. (A, B) Viability comparison of H.pc-TSGA10 cells with H.pc cells treated with or without various concentrations of rapamycin after 48 hours of incubation under normoxia and hypoxia. (C, D) Viability comparison of H.pc-TSGA10 cells to H.pc cells treated with or without different concentrations of cisplatin after 48 hours of incubation. Data are presented as the mean ± SD from n=6 independent experiments. Statistical significance is denoted as ** P < 0.01 and *** P < 0.001 compared to the control.
Figure 6. (A, B) HeLa cells were transfected with 1.5 μg of pcDNA3.1-TSGA10 or pcDNA3.1 and then treated with or without various concentrations of rapamycin (100-800 nM) for 72 hours in normoxia and hypoxia. (C, D) HeLa cells were transfected with different concentrations of pcDNA3.1-TSGA10 or pcDNA3.1 (0.5-2 μg) and then treated with or without 200 nM rapamycin for 72 hours in normoxia and hypoxia (n = 6, mean ± SD, ** P < 0.01, *** P < 0.001 compared to the control).
3.5.	Molecular Modeling and Docking
Molecular modeling and docking approaches were utilized to generate three-dimensional structural models of TSGA10. The TSGA10 sequence was aligned with six template protein structures and modeled using the MODELLER program. Ramachandran plot analysis of the resulting models by PROCHECK revealed that 90.3% of amino acid residues occupied the most favored or additionally allowed regions (Figure 7A), indicating the overall high stereochemical quality of the predicted model. Molecular docking using the HADDOCK web server was subsequently performed to model the interaction between TSGA10 and BCL-2 domains and gain insights into their binding mode (Figure 7B). The total interaction energy, van der Waals energy, and electrostatic energy values were examined to evaluate the TSGA10-BCL2 binding capability. Docking analysis demonstrated that TSGA10 binds BCL-2 more strongly through electrostatic forces than via van der Waals interactions (Table 1).
Figure 7. Structural model evaluation of TSGA10 and its docking to Bcl-2. (A) Ramachandran plot analysis of TSGA10 shows the distribution of residues in the most favored (red), additionally allowed (yellow), generously allowed (pale yellow), and disallowed (white color) regions. (B) The complex structure of TSGA10 docked with Bcl-2 was obtained using computational methods.
  To identify key interacting residues at the TSGA10/BCL-2 interface, per-residue decomposition of the total binding energy was conducted. Intermolecular nonbonded contacts were detected using Discovery Studio, and residue pairs exhibiting favorable interaction energies were analyzed (Table 1). These residues' van der Waals, electrostatic, and total interaction energies were calculated using the "Calculate Interaction Energy" protocol in Discovery Studio, with the CHARMM force field and implicit distance-dependent dielectrics. For all residue pairs examined, electrostatic interactions contributed substantially more than hydrophobic forces to the total interaction energy. This suggests that the TSGA10/BCL-2 binding mode involves charged side chain interactions rather than van der Waals contacts. Among the identified pairs, LYS169-GLU200 and GLU691-ARG207 possessed the strongest electrostatic and overall interaction energies.
Interaction pair	VdW energy (kcal/mol)	Electrostatic energy (kcal/mol)	Interaction energy (kcal/mol)
TSGA10 – BCL-2	-43.0048	-253.236	-296.24
LYS169:HZ1 - ASP196:OD2	-0.86185	-29.5956	-30.4574
LYS169:HZ2 - GLU200:OE2	5.30767	-41.7245	-36.4168
ASP243:OD1 - ARG107:NH2	-1.01023	-17.9569	-18.9671
ARG512:HH11 - SER205:O	0.251528	-10.3206	-10.0691
ARG512:HH11 - MET206:O	-0.65217	-16.9378	-17.5899
GLU513:OE1 - ARG207:HH11	5.61914	-32.2724	-26.6533
LYS532:NZ - GLU200:OE1	-0.23984	-11.2684	-11.5083
ARG539:NH2 - ASP196:OD2	-0.81043	-21.3965	-22.2069
GLU691:OE1 - ARG207:NH2	0.663735	-36.8467	-36.1829














Table 1. Total interaction energy, van der Waals forces, and electrostatic components for the entire TSGA10/BCL-2 complex and the critical residue pairs involved.

4.	Discussion
Our study demonstrates that overexpression of TSGA10 induces autophagy and apoptosis in HeLa cells, suggesting a potential tumor suppressive function of TSGA10. Specifically, we observed increased expression of autophagy-related genes, accumulation of acidic vesicles indicative of autophagic flux, upregulation of pro-apoptotic proteins, and a reduction in cell viability upon TSGA10 overexpression. Although these results provide valuable insights, several limitations require cautious interpretation and highlight the need for further investigation.
First, our experimental approach focused exclusively on overexpression of TSGA10 without complementary loss-of-function studies. To fully elucidate the role of TSGA10 in the regulation of autophagy and apoptosis, it is important to evaluate the effects of TSGA10 knockdown or knockout. Such studies would determine whether suppression of TSGA10 has opposite effects, thereby strengthening the causal relationship between TSGA10 expression and the observed cellular outcomes. Unfortunately, due to budget constraints, we were unable to perform knockdown experiments in the current study.
Second, we found that TSGA10 expression levels were not significantly different between normoxic and hypoxic conditions. This observation suggests that TSGA10 may not be directly regulated by hypoxia-inducible factors or that its overexpression through a strong promoter overrides any physiological regulation by oxygen levels. Previous studies have shown that hypoxia can modulate gene expression through HIF-1α-dependent and -independent pathways (28). Therefore, further studies are required to clarify the regulation of TSGA10 under different oxygen tensions and its interaction with hypoxia-responsive elements.
Third, although we observed a decrease in BCL-2 expression and an increase in the BAX/BCL-2 ratio in TSGA10-overexpressing cells, the mechanistic relationship between TSGA10 and BCL-2 remains unclear. Our molecular docking simulations suggested a possible interaction between TSGA10 and the BH2 domain of BCL-2, which is crucial for its anti-apoptotic function (29). However, computational predictions alone cannot confirm protein-protein interactions. To support this proposed interaction, experimental validation using techniques such as co-immunoprecipitation or proximity ligation assays is required. Furthermore, the reduction in BCL-2 levels could be due to indirect effects of TSGA10 overexpression on upstream signaling pathways rather than direct binding.
Moreover, our assessment of autophagy-related gene expression was limited to a select group of genes, including Beclin1, BNIP3, NIX, ATG5, LC3I, and LC3II. While these markers indicate autophagic activity, a more comprehensive analysis using transcriptomic approaches would allow a more comprehensive understanding of the molecular changes induced by TSGA10 overexpression. High-throughput RNA sequencing could identify additional genes and pathways involved in autophagy regulation and reveal potential compensatory mechanisms.
The connection between autophagy induction and reduced cell viability in TSGA10-overexpressing cells also requires further investigation. Autophagy can play a dual and context-dependent role in cancer, either promoting cell survival under stressful conditions or facilitating cell death when excessively activated (30). To determine whether the autophagy observed in our study contributes to cell death or represents a protective response, experiments with autophagy inhibitors (e.g., 3-methyladenine, bafilomycin A1) or genetic silencing of key autophagy genes (e.g., ATG5, Beclin1) insightful.
Furthermore, our studies were limited to the HeLa cell line, which may limit the generalizability of our results. HeLa cells possess unique characteristics, including high proliferation and genetic instability, which may influence their response to TSGA10 overexpression (31). Expanding our studies to additional cancer cell lines of different origins as well as normal epithelial cells would allow a more comprehensive assessment of the role of TSGA10 in cellular homeostasis and tumorigenesis.
We also acknowledge that, although our molecular docking studies provide evidence for possible interactions between TSGA10 and BCL-2, they cannot replace experimental validation. The energy calculations indicating favorable binding provide a rationale for further investigation but do not confirm a direct interaction. Empirical evidence is needed to establish the biological relevance of this predicted interaction.
A previous study has shown that TSGA10 can inhibit the activity of hypoxia-inducible factor 1-alpha (HIF-1α) by preventing its nuclear localization and transcriptional activity (32). Since HIF-1α regulates genes involved in autophagy and apoptosis (33), it is plausible that TSGA10 indirectly modulates these pathways through HIF-1α inhibition. The interplay between the TSGA10, HIF-1α, and BCL-2 family proteins remains to be elucidated and represents an important avenue for future research.
Budget constraints limited our ability to conduct additional experiments that could answer these critical questions. Despite these limitations, our study provides preliminary evidence for the role of TSGA10 in promoting autophagy and apoptosis in cancer cells. These results contribute to the understanding of TSGA10 as a potential tumor suppressor and highlight its importance in cancer biology.
5.	Conclusion
Although our data suggest that overexpression of TSGA10 stimulates autophagy and apoptosis in HeLa cells, the underlying mechanisms remain to be further elucidated. Future studies should include both gain- and loss-of-function approaches, use comprehensive gene expression analyses, experimentally validate protein-protein interactions, and expand studies to multiple cell lines and in vivo models. Such research will be crucial to fully uncover the role of TSGA10 in cellular homeostasis and to evaluate its potential as a therapeutic target in cancer treatment."