Glycerol-3-Phosphate Dehydrogenase 1-Like serves as a tumor suppressor in colorectal cancer through ATF3-mediated ferroptosis

Glycerol-3-Phosphate Dehydrogenase 1-Like serves as a tumor suppressor in colorectal cancer through ATF3-mediated ferroptosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Glycerol-3-Phosphate Dehydrogenase 1-Like serves as a tumor suppressor in colorectal cancer through ATF3-mediated ferroptosis Changjiang Yang, Long Zhao, Zihan Zhao, Lin Gan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7120750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) continues to be a prevalent malignancy, posing a significant risk to human health. The involvement of Glycerol-3-phosphate dehydrogenase 1-like (GPD1L) in CRC development was suggested by our analysis of clinical samples. However, the role of GPD1L in CRC remains unclear. This study seeks to elucidate the clinical relevance, biological function, and potential molecular mechanisms of GPD1L in CRC. Our results demonstrated that GPD1L expression in CRC tissues was notably lower compared to adjacent normal tissues. This low expression correlated with a poorer prognosis for CRC patients. GPD1L overexpression impeded CRC cell proliferation and migration. Moreover, knockdown of GPD1L could reduce the iron content and lipid oxidation level, increase the antioxidant capacity of cells, and weaken the ferroptosis of CRC cells. Mechanistically, GPD1L affected ferroptosis by affecting the expression level of ATF3, and finally led to the change of the biological behavior of CRC cells. In summary, GPD1L functions as a tumor suppressor, primarily by promoting ferroptosis through ATF3 and affects the malignant phenotype and biological behavior of CRC. This role established GPD1L as a promising prognostic biomarker and a potential therapeutic target for patients with CRC. Glycerol-3-Phosphate Dehydrogenase 1-Like colorectal cancer ferroptosis prognosis biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Summary GPD1L acts as a tumor suppressor by inducing ferroptosis via ATF3, influencing the malignancy and behavior of CRC. This makes GPD1L a promising prognostic biomarker and potential therapeutic target for patients with CRC. Introduction Colorectal cancer (CRC) ranks among the most prevalent malignancies of the digestive system and presents a significant threat to global health. Despite substantial progress in CRC diagnosis and treatment, mortality rates remain persistently high [ 1 ] . Fundamental research at cellular and molecular levels serves as a cornerstone for advancing clinical oncology. Investigating alterations in CRC gene expression, coupled with integrated analysis of RNA, proteins, and cellular functions and phenotypes within tissue samples, offers fresh perspectives on CRC pathogenesis. Decoding the molecular mechanisms driving CRC initiation, progression, metastasis, and drug resistance, while converting these findings into clinical applications, is critical to improving CRC prognosis. Proteins serve as the primary mediators of phenotypic expression, reflecting the physiological state and functional dynamics of cells. Proteomics technology has become an essential instrument in the early diagnosis, prognosis, and monitoring of recurrence and metastasis, as well as in the identification of therapeutic targets for CRC [ 2 – 5 ] . Overall, the comprehensive exploration of proteomics is poised to enhance our understanding of the molecular characteristics of CRC, thereby enabling earlier diagnosis and the development of more precise and effective treatment strategies, which are essential for improving CRC prognosis. The GPD1L protein, a metabolic enzyme, playing a role in glucose and NAD+-dependent energy metabolism, as well as in the mammalian respiratory chain. Predominantly localized in the cytoplasm associated with the plasma membrane, GPD1L interacts with the voltage-gated sodium channel (SCN5A) to modulate Na + influx. Mutations in GPD1L disrupt Na + channel transport, leading to conditions such as Brugada syndrome, sudden infant death syndrome, and various inherited arrhythmia syndromes [ 6 – 9 ] . While previous research on GPD1L has been largely concentrated in the context of arrhythmias, emerging evidence has implicated its involvement in malignancies, including head and neck squamous cell carcinoma [ 10 ] , oropharyngeal cancer [ 11 ] , lung cancer [ 12 ] , hepatocellular carcinoma [ 13 ] and renal cancer [ 14 ] . Nonetheless, its role and underlying mechanisms in CRC remain unexplored. In our research, a thorough analysis integrating transcriptomic and proteomic from both our cohort and public databases identified GPD1L as a promising biomarker and therapeutic target for CRC. Subsequent investigations assessed GPD1L's influence on CRC cell biology, uncovering pivotal downstream genes and associated molecular pathways, thereby offering novel insights into diagnostic and therapeutic strategies for CRC. Materials and Methods Tissue samples Tissue samples for this study were obtained from primary cancer tissues and adjacent non-cancerous tissues of CRC patients who underwent surgical resection. Wayen Biotechnologies (Shanghai, China) executed the label free proteomic analysis based on the tumor tissues and normal tissues from 10 patients. Public database RNAseq data of the TCGA-COAD (Colon adenocarcinoma) and TCGA-READ (Rectum adenocarcinoma) projects were retrieved from the TCGA database. Following the exclusion of samples lacking clinical data, 644 CRC tissue samples and 51 adjacent non-cancerous tissue samples were included, accompanied by relevant clinicopathological data. Other Expression data and clinical follow-up matrices were sourced from the Gene Expression Omnibus database. Cell lines Human CRC cell lines, including SW480 and HCT8, were sourced from the Shanghai Cell Bank of the Chinese Academy of Sciences. All cells had recently been authenticated using the short tandem repeat method and tested for mycoplasma contamination. The last test conducted in September 2024. IHC staining Paraffin sections were immersed in xylene for 10 minutes, followed by gradient dewaxing through a series of ethanol concentrations. Endogenous peroxidase activity was reduced by blocking with hydrogen peroxide for 15 minutes, after which sections were rinsed in PBS. Following antigen retrieval, 5% goat serum was applied for blocking. The blocking solution was then removed, and the primary antibody GPD1L (1:100, Invitrogen, Cat.No.PA5-24216) was applied, with incubation at 37°C for 2 hours or at 4°C overnight. After incubation, the sections were treated with a biotin-labeled secondary antibody. Results were interpreted by two independent pathologists, assessing both staining intensity and distribution. Staining intensity was scored as follows: 0 (negative), 1 (weak positive), 2 (moderate positive), and 3 (strong positive). The staining distribution was graded on a scale of 0 to 4, corresponding to 0–5%, 6%-25%, 26%-50%, 51%-75%, and 76%-100%, respectively. The final score was calculated by multiplying the intensity score by the distribution score. Western blotting Protein extraction was performed using Western and IP cell lysis buffer (Beyotime Biotechnology), and concentrations were measured via the BCA assay kit (Solarbio). Protein samples were prepared with 5×SDS Loading Buffer at a 4:1 ratio, followed by denaturation at 100°C for 5 minutes. Proteins were then separated by SDS-PAGE and transferred onto a 0.45 µm nitrocellulose membrane at a constant 200 mA. Blocking was achieved with TBST containing 5% skim milk. Primary antibodies were diluted according to manufacturer guidelines and incubated overnight at 4°C on a horizontal shaker. Secondary antibody incubation proceeded for 2 hours at room temperature under similar conditions. Protein detection was conducted using an automated exposure system as per the reagent instructions. Lentivirus transfection The GPD1L overexpression and knockdown lentivirus were constructed by Shanghai Genechem Medical Technology. GFP expression became visible 24–48 hours post-infection under a fluorescence microscope. At this stage, Puromycin was introduced during subculture to isolate stably transfected cells. The shRNA interference sequences were as followed. shGPD1L-1: 5’-GCTTAAGAACATCGTAGCTGT-3’ shGPD1L-2: 5’-GGAAGACCATTGAAGAGTTGG-3’ shNC: 5’-TTCTCCGAACGTGTCACGT-3’ RT-qPCR Total RNA was isolated via the Trizol method and subsequently reverse transcribed using the FastKing-RT SuperMix kit (TIANGEN, Cat.No.KR118). The RT-qPCR was carried out utilizing the iTaq Universal SYBR® Green SuperMix (2×) system (Bio-Rad, Cat.No.1725122). Target gene mRNA expression levels were determined using the 2 −ΔΔCt method. The primer sequences are as followed: GPD1L-F:5’-GCCAAGTGTCTACAGCCACCTTC-3’ GPD1L-R:5’-CCCATTCAGCATCTCCTTCTCCAAC-3’ GAPDH-F:5’-CCCCGGTTTCTATAAATTGAGC-3’ GAPDH-R:5’-CACCTTCCCCATGGTGTCT-3’ CCK-8 cell proliferation assay The cell suspension was prepared at a concentration of 1×10⁴ cells/mL and seeded into 96-well plates. At 24-hour intervals, 90 µL of complete medium and 10 µL of CCK-8 reagent were introduced. Following a 2-hour incubation in a 37°C, 5% CO₂ atmosphere, absorbance was recorded at 450 nm to assess cell viability, and a proliferation curve was generated. Colony formation assay Cell suspensions were adjusted to a concentration of 1*10 4 /mL and seeded into 6-well plates at the appropriate volume. Culturing was halted once most colonies reached 50–100 cells. Cells were then fixed with 1 mL of 4% paraformaldehyde for 15 minutes, followed by staining with 1 mL of 0.1% crystal violet for an additional 15 minutes, and air-dried at room temperature. After drying, plates were photographed against a clean white background, and the number of colonies was quantified using ImageJ software. Wound healing assay An appropriate volume of cell suspension was inoculated into a 6-well plate. Once cell confluence reached approximately 90–100%, a longitudinal scratch was made through the center of the culture well, perpendicular to the marked line. Following this, 2mL of low-serum medium (< 1% FBS) was added to sustain culturing. Cell migration was documented at the same site using an inverted microscope, accompanied by image capture. The cell area within the scratch at various time points was measured using ImageJ software, and the wound healing rate was calculated for subsequent statistical analysis. Transwell assay The cell density was adjusted to 5*10⁵/mL, and 200 µL of the cell suspension was added to the upper compartment of the Transwell chamber, while 600 µL of culture medium containing 20% FBS was placed in the lower compartment. The culture plate was incubated at 37°C in a humidified 5% CO 2 atmosphere. After incubation, the chamber was removed, fixed with paraformaldehyde, and stained with 0.1% crystal violet solution. Cells on the upper side of the microporous membrane were carefully removed with a cotton swab. Images were captured using an inverted microscope, and three random fields (100× magnification) were counted per sample. The number of cells migrating through the membrane was quantified using ImageJ software. For the invasion assay, the upper surface of the membrane in the Transwell chamber was coated with Matrigel matrix, while the remaining procedures followed those of the migration assay. Transcriptome sequencing The RNA sequencing procedure encompassed sample analysis, library preparation, quality control, and sequencing, conducted by Beijing Biomarker Technologies Co., Ltd. Qualified RNA samples were subjected to library construction, following these procedures: (1) mRNA was isolated using Oligo(dT)-attached magnetic beads. (2) The isolated mRNA was subsequently fragmented randomly in a fragmentation buffer. (3) First-strand cDNA synthesis was performed using the fragmented mRNA as a template and random hexamers as primers. This was followed by second-strand cDNA synthesis, which involved the addition of PCR buffer, dNTPs, RNase H, and DNA polymerase I. The cDNA was then purified using AMPure XP beads. (4) The resulting double-stranded cDNA underwent end repair, with the addition of adenosine at the ends and ligation to adapters. AMPure XP beads were utilized to select fragments within the size range of 300–400 base pairs. (5) The cDNA library was constructed through several rounds of PCR amplification of the cDNA fragments obtained in step 4. The qualified library was pooled based on pre-designed target data volume and then sequenced on Illumina sequencing platform. After quality control of sequencing data, Clean Data were obtained. The clean data were mapped to the reference genome using HISAT2 to determine the read locations on the reference genome (Homo_sapiens.GRCh38_release95.genome.fa) and to extract characteristic information of the sequenced samples. Based on the reads aligned to the reference genome, transcripts were assembled using StringTie to obtain transcript information for each sample. The number of reads corresponding to each transcript was quantified, followed by expression analyses. Differential expression analysis is processed by DESeq2. Cellular iron content and redox parameter detection Cellular iron content (Solarbio, Cat. No. BC5315), intracellular reduced glutathione (GSH) (Solarbio, Cat. No. BC1175), oxidized glutathione (GSSG) (Solarbio, Cat. No. BC1185), glutathione peroxidase (GSH-Px/GPX) activity (Solarbio, Cat. No. BC1195), malondialdehyde (MDA) levels (Solarbio, Cat. No. BC0025), and thioredoxin reductase (TrxR) activity (Solarbio, Cat. No. BC1155) were quantified according to the manufacturer's protocols. RNA interference Upon reaching 50%-60% cell confluence, siRNA or plasmid and 10µL Lipo2000 should be diluted in 250µL Opti-MEM, mixed gently, and incubated at room temperature for 5 minutes. The resulting complex was then transferred into a 6-well plate and mixed by gentle agitation. After 24 to 48 hours, transfection efficiency can be assessed using Western blot, and additional cellular or molecular biology assays may be conducted as required. siNC 5’-UUCUCCGAACGUGUCACGUTT-3’ siATF3-1 5’-GAGGCGACGAGAAAGAAAUTT-3’ siATF3-2 5’-GCUCAGAUUGAGGAGCUCATT-3’ Mouse xenograft tumor model Six-week-old NOD/SCID mice were used for mouse xenograft tumor model. Prior to tumor implantation, each animal underwent a thorough examination to verify health status and ensure the absence of disease. Subsequently, SW480 cells, at a concentration of 5×10^6, were subcutaneously injected into the nude mice. After a period of four weeks, the animals were euthanized, and the tumors were excised for measurement of their volumes and weights. Enrichment Analysis The R software package ClusterProfiler (version 3.6.3) was employed to conduct gene set enrichment analysis (GSEA) in conjunction with assessments of Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and HALLMARK. The GO analysis included the examination of cellular components (CC), molecular functions (MF), and biological processes (BP). GSEA serves as a computational approach to assess the statistical significance and consistency of differences between two biological conditions based on predefined gene sets. The adjusted p-value and normalized enrichment score (NES) were utilized to identify enriched pathways within each phenotype. Gene sets were considered significantly enriched if they met the criteria of an adjusted p-value of less than 0.05 and a false discovery rate (FDR) of less than 0.25.16. Statistical analysis Statistical analysis For numerical variables, the t-test was applied to assess differences between two groups when the data followed a normal distribution and passed the homogeneity of variance test, while one-way ANOVA was employed for comparisons across three or more groups. If the data adhered to a normal distribution but failed the variance homogeneity test, the Welch t-test was utilized for two-group comparisons, and the Welch one-way ANOVA for three or more groups. When the data deviated from a normal distribution, the Wilcoxon test was used for two-group comparisons, and the Kruskal-Wallis test for three or more groups. Categorical variables were analyzed using the chi-square test when the theoretical frequency exceeded 5 and the sample size was ≥ 40. For theoretical frequencies between 1 and 5 with sample sizes ≥ 40, the continuity correction chi-square test (Yates' correction) was applied. When the theoretical frequency was < 1 or the sample size was < 40, the Fisher exact test was employed. Patient survival evaluations were conducted employing KM methodology and log-rank statistical testing. The correlation between clinical-pathological parameters and patient outcomes underwent assessment via univariate and multivariate Cox regression models. Predictive factors yielding p-values below 0.1 in the univariate evaluation were selected for subsequent multivariate examination. Following this, multivariate Cox analysis was utilized to identify independent prognostic markers. A significance threshold of P < 0.05 was set for all statistical analyses. Results Differential expression of proteins in tumor and normal tissues of CRC To examine protein expression patterns in CRC, we gathered 10 matched sets of colorectal cancer specimens and neighboring healthy tissue samples for comparative protein analysis via label free proteomics. Setting the differential expression threshold at LogFC(fold change) > 1 and statistical significance at P value < 0.05, our analysis revealed 278 proteins showing elevated expression and 485 proteins exhibiting reduced expression within the cancer specimens among the measured proteins (Figure. 1A, B and Supplementary Table 1). To further explore proteins that may play significant roles in the development and progression of CRC, we downloaded gene expression and prognostic data of CRC patients from TCGA database. Survival analysis was performed to identify genes significantly associated with overall survival (OS). The results revealed 1,705 genes significantly correlated with OS in the TCGA CRC cohort, including 631 risk genes (HR > 1) and 1,074 protective genes (HR < 1) (Fig. 1 B). By intersecting the differentially expressed proteins identified through proteomics with these prognostic genes, we identified 4 upregulated genes as risk genes and 24 downregulated genes as protective genes (Fig. 1 C). Among these genes, GPD1L captured our attention due to its high differential expression and strong prognostic relevance (Supplementary Table 2). However, its functional role in the development and progression of CRC remains incompletely understood, warranting further in-depth investigation. GPD1L expression was reduced in CRC compared to normal tissues Utilizing TCGA database resources, we investigated GPD1L gene expression in CRC cohort. We confirmed that GPD1L expression in CRC tissues was markedly lower than in normal tissues (Fig. 1 D). Comparative analysis of paired transcriptome samples further demonstrated a consistent downregulation of GPD1L in CRC tissues relative to their matched normal counterparts (Fig. 1 E). ROC curve analysis yielded an AUC of 0.969 (95% CI = 0.954–0.983), suggesting strong discriminatory capability between tumor and normal tissues (Fig. 1 F). Additional validation of GPD1L’s downregulated expression in CRC came from CPTAC and GEO datasets (GSE89076, GSE110223, GSE113513, and GSE22598) (Fig. 1 G–K). Western blot analysis of GPD1L expression in frozen tissues from CRC tissues and their paired normal colorectal mucosa revealed a marked reduction of GPD1L protein levels in tumor tissues compared to adjacent normal tissues (Fig. 1 L, M). IHC staining of paraffin-embedded sections from 73 CRC tissues and their paired normal tissues confirmed that GPD1L protein levels were also substantially lower in tumor tissues than in normal tissues (Fig. 1 .N, O). Collectively, these results provided strong evidence that GPD1L expression was significantly lower in CRC tissues compared to normal tissues. Low GPD1L expression was linked to an advanced pathological stage and poor prognosis in CRC patients . The TCGA CRC cohort was stratified into low (n = 322) and high expression (n = 322) groups based on the median mRNA expression level of GPD1L, and its association with clinicopathological features in CRC patients was examined. GPD1L expression exhibited a statistically significant correlation with T stage, N stage, M stage, and pathologic overall stage (Table 1 ). Similarly, based on the median IHC staining score, 73 patients were categorized into a GPD1L low-expression group (n = 30) and a high-expression group (n = 43), followed by an analysis of the association between GPD1L protein levels and clinicopathological parameters., significant associations were identified between GPD1L expression and T stage, N stage, M stage, and AJCC stage, with statistically significant differences (Table 2 ). Table 1 The correlation of clinicopathological characteristics and GPD1L expression in TCGA cohort. Characteristics Low expression of GPD1L High expression of GPD1L P value n 322 322 Gender, n (%) 0.693 Female 153 (23.8%) 148 (23%) Male 169 (26.2%) 174 (27%) Age, n (%) 0.203 65 192 (29.8%) 176 (27.3%) Pathologic T stage, n (%) 0.014 T1 6 (0.9%) 14 (2.2%) T2 51 (8%) 60 (9.4%) T3 214 (33.4%) 222 (34.6%) T4 48 (7.5%) 26 (4.1%) Pathologic N stage, n (%) 0.038 N0 170 (26.6%) 198 (30.9%) N1 77 (12%) 76 (11.9%) N2 71 (11.1%) 48 (7.5%) Pathologic M stage, n (%) 0.039 M0 237 (42%) 238 (42.2%) M1 55 (9.8%) 34 (6%) Pathologic stage, n (%) 0.071 Stage I 49 (7.9%) 62 (10%) Stage II 116 (18.6%) 122 (19.6%) Stage IV 56 (9%) 34 (5.5%) Stage III 92 (14.8%) 92 (14.8%) Table 2 The correlation of clinicopathological characteristics and GPD1L expression in IHC cohort. Characteristic Low High p n 30 43 Gender, n (%) 0.612 Female 12 (16.4%) 21 (28.8%) Male 18 (24.7%) 22 (30.1%) Age, n (%) 0.281 65 10 (13.7%) 21 (28.8%) T, n (%) 0.002 T1&T2 1 (1.4%) 16 (21.9%) T3&T4 29 (39.7%) 27 (37%) N, n (%) 0.026 N0 13 (17.8%) 31 (42.5%) N1&N2 17 (23.3%) 12 (16.4%) M, n (%) 0.003 M0 22 (30.1%) 42 (57.5%) M1 8 (11%) 1 (1.4%) AJCC, n (%) 0.022 I&II 12 (16.4%) 30 (41.1%) III&IV 18 (24.7%) 13 (17.8%) We evaluated GPD1L’s prognostic significance in predicting OS, DSS, and PFI outcomes across CRC cases. KM survival assessment using TCGA data demonstrated enhanced OS, DSS, and PFI in patients expressing higher GPD1L levels (Fig. 1 P–R). Through univariate and multivariate examinations, lower GPD1L emerged as an autonomous risk indicator for CRC patients regarding OS (Table 3 ) and DSS (Supplementary Table 3), as well as PFI (Supplementary Table 4). External validation via GEO datasets (GSE14333, GSE17536) showed associations between heightened GPD1L levels and better DFS (Fig. 1 S, T). These findings suggest that enhanced low GPD1L expression indicates poorer CRC patient outcomes. Table 3 Univariate and multivariate Cox analyses of factors affecting the OS of CRC patients in the TCGA database Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 640 T1&T2 131 Reference Reference T3&T4 509 2.468 (1.327–4.589) 0.004 2.285 (1.035–5.046) 0.041 Pathologic N stage 639 N0 367 Reference Reference N1&N2 272 2.627 (1.831–3.769) < 0.001 0.403 (0.153–1.060) 0.066 Pathologic M stage 563 M0 474 Reference Reference M1 89 3.989 (2.684–5.929) < 0.001 2.314 (1.433–3.738) < 0.001 Pathologic stage 622 Stage I&Stage II 348 Reference Reference Stage III&Stage IV 274 2.988 (2.042–4.372) < 0.001 5.242 (1.794–15.317) 0.002 Gender 643 Female 301 Reference Male 342 1.054 (0.744–1.491) 0.769 Age 643 65 367 1.939 (1.320–2.849) < 0.001 2.617 (1.679–4.080) < 0.001 GPD1L 643 Low 321 Reference Reference High 322 0.560 (0.392–0.799) 0.001 0.633 (0.424–0.946) 0.026 GPD1L affected CRC cell biological function In this study, shRNAs were designed to target GPD1L expression in cells. These shRNAs were transfected into SW480 and HCT8 cell lines to generate stable GPD1L knockdown models. Results from qPCR and Western blot analyses confirmed a significant reduction in both mRNA and protein levels of GPD1L in all treated cells. Additionally, GPD1L-overexpressing SW480 and HCT8 cell lines were established via transfection with GPD1L overexpression plasmids (Fig. 2 A, B). The CCK-8 assay demonstrated that GPD1L overexpression markedly suppressed the proliferation of SW480 and HCT8 cells. Conversely, GPD1L knockdown significantly promoted cell proliferation in both SW480 and HCT8 lines (Fig. 2 C). The colony formation assay further confirmed that increased GPD1L expression significantly reduced colony formation, while decreased GPD1L expression led to a notable enhancement in clonogenic capacity (Fig. 2 D, E). In the wound healing assay, overexpression of GPD1L resulted in a significantly slower scratch healing rate compared to the control group, whereas GPD1L knockdown accelerated the healing process (Fig. 2 F, G). Migration assays revealed that GPD1L overexpression significantly impaired the migratory ability of SW480 and HCT8 cells, while its knockdown enhanced cell migration. Similarly, invasion assays showed that GPD1L overexpression significantly reduced invasive capabilities, whereas GPD1L knockdown had the opposite effect, markedly increasing the invasion potential of both cell lines (Fig. 2 H, I). To evaluate GPD1L’s impact on colon cancer cells within living organisms, we developed tumor xenograft models. Following the implantation of GPD1L-depleted cells (n = 5), we observed increased tumor dimensions and mass (Fig. 2 J, K). Furthermore, Ki-67 expression levels were higher in shGPD1L cohorts relative to shNC controls (Fig. 2 L). The potential biological mechanism of GPD1L involved in the malignant progression of CRC To elucidate GPD1L’s biological functions in CRC, we determined genes showing a significant correlation with GPD1L, illustrated in the heatmap visualization (top25) (Fig. 3 A). Additionally, the following were found to be predominantly linked to co-expressed genes that exhibited a correlation coefficient > 0.5 with GPD1L, as indicated by the GO term annotation: purine-containing compound metabolic process, ribose phosphate metabolic process, purine nucleotide metabolic process, ribonucleotide metabolic process, mitochondrial matrix, magnesium ion binding and oxidoreductase activity. A review of the KEGG pathways showed the enrichment of the majority of the GPD1L-associated genes in the following: Phosphatidylinositol signaling system, Inositol phosphate metabolism, Propanoate metabolism and the Citrate cycle (TCA cycle) (Fig. 3 C). Additionally, 1250 DEGs were found between the low- and high-expression groups with 95 and 1155 up- and down-regulated genes, respectively, in the GPD1L high-expression subgroup (Fig. 3 B). Subsequently, we conducted GO and KEGG pathway analyses. A bubble map revealed the enrichment of upregulated DEGs in the glucuronate metabolic process, uronic acid metabolic process, cellular glucuronidation, glucuronosyltransferase activity, sodium ion transmembrane transporter activity, secondary active transmembrane transporter activity for GO pathway analysis, and Porphyrin metabolism, Pentose and glucuronate interconversions, Ascorbate and aldarate metabolism for KEGG pathway analysis (Fig. 3 D). Additionally, GSEA results demonstrated the negative correlation of GPD1L expression with the IL6-JAK-STAT3 Signaling, TNFα Signaling Via NF-Κb, angiogenesis, apical junction, KRAs Signaling, epithelial mesenchymal transition in Hallmark pathways, cell fate commitment, external encapsulating structure organization, glycosaminoglycan binding, serine hydrolase activity, collagen containing extracellular matrix, extracellular matrix structural constituent in GO and focal adhesion, hedgehog signaling pathway, basal cell carcinoma, ECM receptor interaction, cell adhesion molecules cams (Fig. 3 E-G). Overall, these findings point to the potential involvement of GPD1L in the CRC malignant progression. Based on the aforementioned bioinformatics analysis, we hypothesized that GPD1L, as a biochemical enzyme, might play a significant role in tumor metabolism, metal ion transport and oxidoreductase activity, thereby determining cell fate and malignant biological phenotypes such as invasion and migration. GPD1L affected ferroptosis by affecting the expression level of ATF3. To further investigate the biological functions of GPD1L, we conducted transcriptome sequencing. This approach aims to elucidate the molecular mechanisms and downstream pathways regulated by GPD1L, providing deeper insights into its role in CRC progression. Transcriptome sequencing was performed on the stably transfected human CRC cell line SW480, with GPD1L overexpression (GPD1L-OE) and its control (Vector), as well as on the GPD1L knockdown cell line (shGPD1L) and its control (shNC). Differential gene expression was screened using Fold-change > 1.5 and FDR < 0.05 as the criteria. In the GPD1L overexpression group, 347 genes exhibited significant expression changes compared to the control, with 47 up-regulated and 300 down-regulated (Fig. 4 A). In the knockdown group, 209 genes showed significant changes, with 57 up-regulated and 152down-regulated (Fig. 4 B). This study identified 4 molecules with expression positively correlated with GPD1L by intersecting the upregulated molecules in the GPD1L overexpression group and the downregulated molecules in the GPD1L knockdown group. Similarly, intersecting the downregulated molecules in the GPD1L overexpression group with the upregulated molecules in the GPD1L knockdown group revealed 4 molecules negatively correlated with GPD1L expression (Fig. 4 C). Among these, ATF3, a ferroptosis-inducing factor, has captured our attention. Ferroptosis is an iron-dependent, non-apoptotic form of cell death that differs from traditional cell death mechanisms such as apoptosis, necrosis, and autophagy. It is characterized by unique biochemical features and molecular mechanisms, involving dysregulated iron metabolism, lipid peroxidation, glutathione depletion, and GPX4 inactivation, all of which contribute to redox homeostasis imbalance. This aligns closely with our preliminary bioinformatics analysis. Therefore, we hypothesize that GPD1L may regulate ferroptosis through its modulation of ATF3. To validate this hypothesis, we examined the correlation between GPD1L and ATF3 expression in CRC samples. The expression of ATF3 and GPD1L exhibits a strong correlation, suggesting a potential regulatory relationship between these two genes. This consistency in expression patterns further supports our hypothesis that GPD1L may influence ferroptosis, through its interaction with ATF3(Fig. 4 D). We investigated the regulatory impact of GPD1L expression on ATF3 and key ferroptosis-associated markers at the molecular level. Overexpression of GPD1L elevated the levels of ferroptosis-promoting molecules, including ATF3, ACSL4, and TP53, while reducing the expression of the ferroptosis-inhibitor GPX4. In contrast, GPD1L knockdown led to decreased levels of ATF3, ACSL4, and TP53, alongside an increase in GPX4 expression (Fig. 4 E). Furthermore, siRNAs were designed to interfere ATF3 expression in cells for further rescue experiments. Our experiments validated that the suppression of ferroptosis in CRC cells, induced by reduced GPD1L levels, was mediated through decreased ATF3 expression. This effect was reversible by upregulating ATF3 expression levels (Fig. 4 F-I). GPD1L induced ferroptosis in CRC. Intracellular iron levels serve as an indicator of cellular ferroptosis, with increased iron content observed during ferroptosis. By measuring changes in intracellular iron following ferroptosis induced by GPD1L expression modulation, the influence of GPD1L on ferroptosis can be assessed. Experimental data demonstrated that erastin-induced ferroptosis in CRC cells resulted in elevated intracellular iron levels in SW480 and HCT8 cell lines overexpressing GPD1L, compared to the control group. Conversely, GPD1L knockdown in SW480 and HCT8 cell lines led to reduced intracellular iron content relative to controls (Fig. 5 A). GPX activity serves as a reliable indicator of intracellular ferroptosis, with decreased GPX activity correlating with elevated ferroptosis levels. Following erastin-induced ferroptosis in CRC cells, GPD1L-overexpressing SW480 and HCT8 cell lines exhibited lower intracellular GPX activity compared to the control group. In contrast, GPD1L-knockdown SW480 and HCT8 cell lines showed higher GPX activity than the control group (Fig. 5 B). GSH levels are inversely associated with ferroptosis. Monitoring the intracellular GSH content in GPD1L-regulated cells allows assessment of GPD1L's impact on ferroptosis. Experimental data revealed that, following erastin-induced ferroptosis in CRC cells, intracellular GSH levels were reduced in GPD1L-overexpressing SW480 and HCT8 cell lines compared to the control group. Conversely, GSH levels were elevated in GPD1L-knockdown SW480 and HCT8 cells relative to controls (Fig. 5 C). GSSG levels were also a marker for ferroptosis, with elevated intracellular oxidative stress and GSSG content indicating increased ferroptosis. The experimental results demonstrated that, following erastin-induced ferroptosis in CRC cells, GPD1L-overexpressing SW480 and HCT8 cell lines exhibited higher intracellular GSSG levels compared to the control group, whereas GPD1L-knockdown SW480 and HCT8 cell lines showed lower intracellular GSSG levels relative to the control group (Fig. 5 D). Oxygen free radicals produced during cellular metabolic processes can react with unsaturated fatty acids in lipids, leading to lipid peroxidation and the formation of various complex compounds, with MDA being a key marker. Measuring MDA levels provides an accurate indication of lipid peroxidation in tissue cells. As ferroptosis increases, lipid peroxidation intensifies, making MDA detection a reliable indirect measure of ferroptosis. Following erastin-induced ferroptosis in CRC cells, results indicated that intracellular MDA levels were elevated in SW480 and HCT8 cells overexpressing GPD1L compared to the control group, whereas MDA levels were reduced in GPD1L knockdown cells (Fig. 5 E). Thioredoxin reductase (TrxR) shares functional similarities with glutathione reductase, catalyzing the reduction of GSSG to GSH, and plays a central role in the glutathione redox cycle. Reduced TrxR content or activity leads to decreased GSH levels, weakening the cell's ability to counteract oxidative stress. As such, TrxR activity assays serve as indicators of intracellular oxidative stress and ferroptosis. Following erastin-induced ferroptosis in CRC cells, results demonstrated that TrxR activity was lower in GPD1L-overexpressing SW480 and HCT8 cell lines compared to controls, while GPD1L-knockdown SW480 and HCT8 cell lines exhibited higher TrxR activity than their respective controls (Fig. 5 F). These results indicate that GPD1L played a role in regulating ferroptosis in colon cancer cells. Discussion CRC ranks among the most prevalent gastrointestinal malignancies, accounting for nearly 900,000 deaths annually worldwide [ 15 ] . Although early detection, diagnosis, and treatment have significantly lowered CRC mortality, a substantial number of cases, particularly in advanced stages, remain resistant to current therapeutic interventions. Increasing evidence highlights CRC as a highly heterogeneous disease, marked by the gradual accumulation of genetic and epigenetic alterations, with significant inter- and intra-tumor variability. The tumor’s protein expression profile dictates its progression, therapeutic response, and prognosis. Advancing the understanding of CRC’s molecular landscape is essential for achieving earlier diagnosis and more targeted, effective treatments, ultimately improving patient outcomes. Previous research has demonstrated a marked downregulation of GPD1L expression in head and neck squamous cell carcinoma (HNSCC), with low GPD1L levels strongly associated with higher rates of postoperative local recurrence and poor long-term prognosis in HNSCC patients [ 10 ] . Liu et al [ 11 ] reported that GPD1L expression served as a predictor of lymph node metastasis in oropharyngeal cancer. Fan et al [ 12 ] identified GPD1L as a potential diagnostic and prognostic marker for lung cancer, with significantly reduced expression independently correlating with poor long-term survival. Similarly, Liu et al [ 14 ] observed reduced GPD1L levels in renal cancer tissues compared to adjacent normal tissues, noting a positive correlation between GPD1L expression and renal cancer prognosis. These results align with the present study's conclusions regarding CRC. Our study demonstrated that GPD1L expression at both mRNA and protein levels was markedly downregulated in CRC tissues, indicating its potential as a diagnostic marker and its role as a tumor suppressor in CRC pathogenesis. Additionally, GPD1L expression was significantly associated with clinicopathological parameters, including TNM stage, while survival analysis identified GPD1L as an independent prognostic factor for long-term outcomes in CRC. This study provides comprehensive evidence of GPD1L’s expression and clinical relevance in CRC, supporting its potential as both a diagnostic and prognostic biomarker for CRC patients. Previous studies have suggested that alterations in GPD1L expression influence the biological behavior of both benign and malignant cells. For instance, Hao et al [ 16 ] reported that GPD1L knockdown stimulated the proliferation and collagen synthesis of atrial fibroblasts, while Liu et al [ 14 ] found that GPD1L overexpression in renal cancer cells markedly inhibited cell proliferation, migration, and invasion, while promoting apoptosis. However, no studies have previously investigated the role of GPD1L in CRC. The present study confirmed that modulating GPD1L expression significantly impacted the biological behavior of CRC cells. GPD1L functions as a tumor suppressor gene in CRC development, providing cellular and molecular evidence that supports prior clinical findings and establishes a phenotypic foundation for further mechanistic exploration. In our study, transcriptome sequencing of stably transfected cells following GPD1L expression modulation revealed that GPD1L significantly influenced the expression of ATF3, a ferroptosis-promoting molecule. Further analysis demonstrated that altering GPD1L expression markedly affected iron content, lipid peroxidation, GSH levels, and the expression of key ferroptosis regulators, including ATF3, ACSL4, TP53, and GPX4, in CRC cells treated with erastin. These results confirm that GPD1L plays a regulatory role in CRC cell ferroptosis. Specifically, GPD1L overexpression promotes ferroptosis, while its knockdown inhibits this process. Complementary experiments modulating ATF3 expression showed that changes in ATF3 levels can reverse GPD1L-induced alterations in ferroptosis-related factors, indicating that ATF3 serves as a critical pathway in the GPD1L-driven regulation of CRC ferroptosis. ATF3 is a stress-responsive transcription factor and a central component of the cellular adaptive response network. It plays a role in processes such as metabolic regulation and tumor immunity by binding to cAMP response elements in target genes, and its involvement in the pathogenesis of various malignancies is well-documented. Recognized for its role in promoting ferroptosis, ATF3 has been extensively studied [ 17 ] . Research indicates that ATF3 expression is reduced in CRC [ 18 ] . Both in vitro and in vivo experiments have demonstrated that ATF3 overexpression inhibits CRC cell proliferation, migration, and invasion [ 19 ] , while also inducing apoptosis [ 20 – 22 ] . Zhao et al [ 23 ] reported that LncRNA DLEU1 suppressed ferroptosis in glioblastoma by binding to ZFP36, which accelerated the degradation of ATF3 mRNA, thereby increasing SLC7A11 expression. Qian et al [ 24 ] identified Shikonin as an inhibitor of non-small cell lung cancer proliferation, achieved by upregulating ATF3 expression and inducing ferroptosis via histone acetylation. Wang et al [ 25 ] provided strong evidence that ATF3 enhanced erastin-induced ferroptosis. Shen et al [ 26 ] demonstrated that PARP inhibitors can enhance anti-tumor immune responses and ferroptosis in CRC through the cGAS pathway and the ATF3/SLC7A11/GPX4 axis. These studies collectively indicate that ATF3 induces ferroptosis. The upstream regulatory mechanisms governing ATF3 expression are complex. As an adaptive response gene, ATF3 can be induced by various stimuli, such as cytokines, pharmacological agents, and stress-related signals, subsequently modulating the expression of target genes [ 27 ] . However, the interaction between GPD1L and ATF3 has not been previously explored. This study provides the first experimental evidence that GPD1L functions as an upstream regulator of ATF3 expression and regulated ferroptosis in CRC, marking a significant novel contribution. Despite strengths, this study presents certain limitations and areas for improvement. First, the investigation of GPD1L's role in regulating CRC ferroptosis via ATF3 requires further refinement. Beyond the biochemical assays already conducted, such as measuring cellular iron content and various redox parameters, ferroptosis detection could be enhanced by employing transmission or scanning electron microscopy to capture the distinctive morphological changes in cells and mitochondria associated with ferroptosis. Moreover, the mechanism by which GPD1L modulates ATF3 expression remains unresolved. Further studies are necessary to elucidate whether the regulation is direct or indirect, necessitating deeper mechanistic exploration. In conclusion, this study comprehensively analyzed the alterations in GPD1L expression and their clinical significance in CRC, confirming the impact of GPD1L on the biological behavior of CRC cells. Additionally, the underlying mechanism of GPD1L's regulation of CRC cell ferroptosis via ATF3 was thoroughly investigated. These findings suggest that GPD1L could serve as a novel molecular marker and potential therapeutic target for CRC. Declarations Availability of data and materials The data in this study mainly come from public databases: The Gene Expression Omnibus (GEO) database: http://www.ncbi.nlm.nih.gov/geo; The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov/. Other original data presented in the study are included in the article/supplementary materials, the RNA sequencing data relating to GPD1L transcriptomics was uploaded to GEO database (GSE296448), and further inquiries can be directed to the corresponding authors. Funding : This work was supported by the Postdoctoral Scientific Research Startup Fund in 2024 of the First Affiliated Hospital of Zhengzhou University. Project Code: 72410. Author Contributions C.J.Y. contributed to the conception and design of the study. C.J.Y and Z.L. extracted and analyzed the data. C.J.Y and Z.H.Z conducted experiments and drafted the manuscript. L.G. completed critical review and funding support. All authors have read and approved the final version of the manuscript. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the First Affiliated Hospital of Zhengzhou University. The animal study was reviewed and approved by the First Affiliated Hospital of Zhengzhou University. Written informed consent was obtained from the individuals for the publication of any potentially identifiable images or data included in this article. Conflicts of interest: The authors declare no conflict of interest. References Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies[J]. Nat Rev Gastroenterol Hepatol. 2019;16(12):713–32. 10.1038/s41575-019-0189-8 . Shao Y, Xu K, Zheng X, Zhou B, Zhang X, Wang L, et al. Proteomics profiling of colorectal cancer progression identifies PLOD2 as a potential therapeutic target[J]. Cancer Commun (Lond). 2022;42(2):164–9. 10.1002/cac2.12240 . Tang M, Zeng L, Zeng Z, Liu J, Yuan J, Wu D, et al. Proteomics study of colorectal cancer and adenomatous polyps identifies TFR1, SAHH, and HV307 as potential biomarkers for screening[J]. 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Role of activating transcription factor 3 (ATF3) in endoplasmic reticulum (ER) stress-induced sensitization of p53-deficient human colon cancer cells to tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis through up-regulation of death receptor 5 (DR5) by zerumbone and celecoxib[J]. J Biol Chem. 2014;289(31):21544–61. 10.1074/jbc.M114.558890 . Zhao J, Yang S, Lv C, Liu Y. Cancer-associated fibroblasts suppressed ferroptosis in glioblastoma via upregulating lncRNA DLEU1[J]. Am J Physiol Cell Physiol. 2023;324(5):C1039–52. 10.1152/ajpcell.00454.2022 . Qian X, Zhu L, Xu M, Liu H, Yu X, Shao Q, et al. Shikonin suppresses small cell lung cancer growth via inducing ATF3-mediated ferroptosis to promote ROS accumulation[J]. Chem Biol Interact. 2023;382:110588. 10.1016/j.cbi.2023.110588 . Wang L, Liu Y, Du T, Yang H, Lei L, Guo M, et al. ATF3 promotes erastin-induced ferroptosis by suppressing system Xc()[J]. Cell Death Differ. 2020;27(2):662–75. 10.1038/s41418-019-0380-z . Shen D, Luo J, Chen L, Ma W, Mao X, Zhang Y, et al. PARPi treatment enhances radiotherapy-induced ferroptosis and antitumor immune responses via the cGAS signaling pathway in colorectal cancer[J]. Cancer Lett. 2022;550:215919. 10.1016/j.canlet.2022.215919 . Thompson MR, Xu D, Williams BR. ATF3 transcription factor and its emerging roles in immunity and cancer[J]. J Mol Med (Berl). 2009;87(11):1053–60. 10.1007/s00109-009-0520-x . Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.pdf SupplementaryTable1.xlsx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7120750","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501203249,"identity":"e992706d-e57e-48d8-a0c5-381d1831d27e","order_by":0,"name":"Changjiang Yang","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Changjiang","middleName":"","lastName":"Yang","suffix":""},{"id":501203250,"identity":"6399e5f6-6b12-4cdc-ae1a-5db79963a7af","order_by":1,"name":"Long Zhao","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhao","suffix":""},{"id":501203251,"identity":"cc234b60-30e8-4cba-951f-e2b8372c33cb","order_by":2,"name":"Zihan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACHsYHHwzYeOyPNzCDuIwNRGhhNpxRwSfHcOYA8VrYpDnOyBkz3EggUot8/9lj0oxtZomNM98eNuZhsJHdcID52QN8WhgbziVbF7alJTZL5yUn8zCkGW84wGZugE8LM2OP4e2ZbccS26RzjA/zMBxO3HCAh00CnxY2Zh4Dad62/4k9kmdAWv4T1sLDxmMkzXOGzVhCgscY6LADhLVI8PAYAwOZTc6AJy/ZcI5BsvHMw2xmeLXI958xBEelAfvZwxJvKuxk+443P8OrBdmNQAwKKmYi1UO1jIJRMApGwSjAAgAuf0LwlWbjZgAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Zhao","suffix":""},{"id":501203252,"identity":"ce84a44c-2f62-41cf-9109-f191caa81c1c","order_by":3,"name":"Lin Gan","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Gan","suffix":""}],"badges":[],"createdAt":"2025-07-14 11:38:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7120750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7120750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89976610,"identity":"e2129af0-5816-4deb-9047-d6a852c57b49","added_by":"auto","created_at":"2025-08-27 06:04:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13916788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPD1L was aberrantly down-regulated in CRC tissues and associated with poorer prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of differentially expressed proteins between CRC tissues and matched adjacent normal tissues.\u003c/p\u003e\n\u003cp\u003eB. Volcano plot of hazard ratios showed the prognosis-related genes based on univariate COX regression analysis in TCGA database (high risk genes: HR\u0026gt;1, p\u0026lt;0.05, low risk genes: HR\u0026lt;1, p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eC. Venn Diagram demonstrated the prognostic values of differentially expressed proteins.\u003c/p\u003e\n\u003cp\u003eD and E. GPD1L mRNA expression was downregulated in CRC compared with normal tissues based on TCGA.\u003c/p\u003e\n\u003cp\u003eF.ROC analysis of GPD1L in the diagnosis of CRC.\u003c/p\u003e\n\u003cp\u003eG. GPD1L protein expression was downregulated in CRC compared with normal tissues based on CPTAC.\u003c/p\u003e\n\u003cp\u003eH-K. GPD1L mRNA expression was downregulated in CRC compared with normal tissues based on GEO.\u003c/p\u003e\n\u003cp\u003eL and M. Western blotting to detect GPD1L expression in paired normal and CRC tissues.\u003c/p\u003e\n\u003cp\u003eN and O IHC staining showing GPD1L protein expression was downregulated in CRC samples compared with normal tissues.\u003c/p\u003e\n\u003cp\u003eP–R. The Kaplan-Meier curves show that CRC patients with a lower expression of GPD1L had a shorter OS time, DSS time, and PFI time.\u003c/p\u003e\n\u003cp\u003eS and T. Kaplan–Meier plot for GPD1L expression to evaluate the probability of DFS based on GEO cohort. (ns represents no significance, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/43c889957f07d31e26583adb.png"},{"id":89976612,"identity":"910de2c6-1590-45c4-a347-8a76d6b89b9b","added_by":"auto","created_at":"2025-08-27 06:04:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29660775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPD1L was involved in cell cell proliferation, colony formation, migration, and invasion of CRC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA and B. RT-qPCR and Western blotting analyses of GPD1L protein levels in HCT8 and SW480 cells.\u003c/p\u003e\n\u003cp\u003eC. CCK-8 assay to evaluate the proliferation of HCT8 and SW480 cells in vitro.\u003c/p\u003e\n\u003cp\u003eD and E. Colony formation of HCT8 and SW480. The number of colonies formed was counted by ImageJ.\u003c/p\u003e\n\u003cp\u003eF and G. Wound healing assays to evaluate the ability of migration and invasion of HCT8 and SW480. The number of cells were counted by ImageJ.\u003c/p\u003e\n\u003cp\u003eH and I. Transwell assays to evaluate the ability of migration and invasion of HCT8 and SW480.\u003c/p\u003e\n\u003cp\u003eJ and K. Control SW480 cells or GPD1L-knockdown SW480cells were implanted in NOD/SCID mice. Tumors were resected and measured 15 days later.\u003c/p\u003e\n\u003cp\u003eL. Ki67 staining of tumor tissue from the control or GPD1L-knockdown group (ns represents no significance, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/c4bbe95d7139296a6fd88c92.png"},{"id":89976616,"identity":"130c5215-203c-4148-a18c-20f1629f46db","added_by":"auto","created_at":"2025-08-27 06:04:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5816483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe potential biological mechanism of GPD1L involved in the malignant progression of CRC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Heatmap illustrating the top 25 genes positively correlated with GPD1L in CRC.\u003c/p\u003e\n\u003cp\u003eB. Differentially expressed genes (DEGs) between the low and high GPD1L expression subgroups in CRC as shown via a volcano map. Red represents high expression, and blue represents low expression.\u003c/p\u003e\n\u003cp\u003eC. GO and KEGG pathway analysis of GPD1L co-expressed gene with R\u0026gt;0.5(spearman).\u003c/p\u003e\n\u003cp\u003eD. GO and KEGG Pathway enrichment analyses for upregulated and downregulated DEGs.\u003c/p\u003e\n\u003cp\u003eE-G. GSEA enrichment plots showed correlated pathways of GPD1L(ns represents no significance, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/2dc6dbe1197777cda940b721.png"},{"id":89975180,"identity":"f8c2a39b-6985-4122-abfe-3ae3a8715b9e","added_by":"auto","created_at":"2025-08-27 05:56:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14560159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPD1L affected ferroptosis by affecting the expression level of ATF3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The volcano plot of DEGs between cells with GPD1L overexpression and control cells revealed by transcriptome sequencing.\u003c/p\u003e\n\u003cp\u003eB. The volcano plot of DEGs between GPD1L-knockdown cells and control cells revealed by transcriptome sequencing.\u003c/p\u003e\n\u003cp\u003eC. The Venn diagram of DEGs: genes that were upregulated in cells with GPD1L overexpression and downregulated in GPD1L-knockdown cells, and genes that were downregulated in cells with GPD1L overexpression and upregulated in GPD1L-knockdown cells.\u003c/p\u003e\n\u003cp\u003eD. Immunofluorescence staining of paraffin sections of CRC showed that the expressions of ATF3 and GPD1L are correlated.\u003c/p\u003e\n\u003cp\u003eE. The effect of GPD1L on the expression level on key regulators of ferroptosis.\u003c/p\u003e\n\u003cp\u003eF and G. Changes in ferroptosis regulators after transfecting the ATF3 overexpression plasmid in the stably transfected cell lines SW480 and HCT8 with GPD1L knockdown.\u003c/p\u003e\n\u003cp\u003eH and I. Changes in ferroptosis regulators after transfecting siATF3 in the stably transfected cell lines SW480 and HCT8 with GPD1L overexpression.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/c91acb3eda5db1d8959327de.png"},{"id":89976622,"identity":"ddbd26d6-adc4-4439-977f-4ecc66a69e53","added_by":"auto","created_at":"2025-08-27 06:04:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3044683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effects of GPD1L expression on ferroptosis-related indexes in colon cancer cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The expression of GPD1L affected the iron content in colon cancer cells. B. The expression of GPD1L affected the GPX activity in colon cancer cells.\u003c/p\u003e\n\u003cp\u003eC. The expression of GPD1L affected the GSH level in colon cancer cells.\u003c/p\u003e\n\u003cp\u003eD. The expression of GPD1L affected the GSSG level in colon cancer cells.\u003c/p\u003e\n\u003cp\u003eE. The expression of GPD1L affected the MDA level in colon cancer cells.\u003c/p\u003e\n\u003cp\u003eF. The expression of GPD1L affected the TrxR level in colon cancer cells. (ns represents no significance, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/ca3f4ed70fee8a77865d6319.png"},{"id":99798291,"identity":"bf443852-cf19-4c4f-a334-df00004a5732","added_by":"auto","created_at":"2026-01-08 13:47:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":63079401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/67798a13-7d37-4fea-ba50-71c43633b7a9.pdf"},{"id":89975163,"identity":"14b7102f-02f5-488b-ac4e-22c8349b7a8b","added_by":"auto","created_at":"2025-08-27 05:56:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":394629,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/688e738f80ced90907e0fbbd.pdf"},{"id":89975166,"identity":"09bf2ef8-fcc9-47e4-916e-73dee09d8193","added_by":"auto","created_at":"2025-08-27 05:56:46","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1955754,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/9081801221f18f2bdc155adf.xlsx"},{"id":89975169,"identity":"5188dd43-8c47-4977-b67d-6b10f0e5a73e","added_by":"auto","created_at":"2025-08-27 05:56:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23429,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/1967d0ec6be8bd80308a003a.docx"},{"id":89975189,"identity":"2764b9d5-ba7b-468a-b1d9-015d8653d200","added_by":"auto","created_at":"2025-08-27 05:56:47","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21947,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/a3802a198ea343f6217ba858.docx"},{"id":89977923,"identity":"1fef32e0-c425-4cb8-bee2-d5f83b3036ba","added_by":"auto","created_at":"2025-08-27 06:12:46","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":22006,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7120750/v1/6b03f72674eac0101a41271b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Glycerol-3-Phosphate Dehydrogenase 1-Like serves as a tumor suppressor in colorectal cancer through ATF3-mediated ferroptosis","fulltext":[{"header":"Summary ","content":"\u003cp\u003eGPD1L acts as a tumor suppressor by inducing ferroptosis via ATF3, influencing the malignancy and behavior of CRC. This makes GPD1L a promising prognostic biomarker and potential therapeutic target for patients with CRC.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks among the most prevalent malignancies of the digestive system and presents a significant threat to global health. Despite substantial progress in CRC diagnosis and treatment, mortality rates remain persistently high\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Fundamental research at cellular and molecular levels serves as a cornerstone for advancing clinical oncology. Investigating alterations in CRC gene expression, coupled with integrated analysis of RNA, proteins, and cellular functions and phenotypes within tissue samples, offers fresh perspectives on CRC pathogenesis. Decoding the molecular mechanisms driving CRC initiation, progression, metastasis, and drug resistance, while converting these findings into clinical applications, is critical to improving CRC prognosis.\u003c/p\u003e\u003cp\u003eProteins serve as the primary mediators of phenotypic expression, reflecting the physiological state and functional dynamics of cells. Proteomics technology has become an essential instrument in the early diagnosis, prognosis, and monitoring of recurrence and metastasis, as well as in the identification of therapeutic targets for CRC \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Overall, the comprehensive exploration of proteomics is poised to enhance our understanding of the molecular characteristics of CRC, thereby enabling earlier diagnosis and the development of more precise and effective treatment strategies, which are essential for improving CRC prognosis.\u003c/p\u003e\u003cp\u003eThe GPD1L protein, a metabolic enzyme, playing a role in glucose and NAD+-dependent energy metabolism, as well as in the mammalian respiratory chain. Predominantly localized in the cytoplasm associated with the plasma membrane, GPD1L interacts with the voltage-gated sodium channel (SCN5A) to modulate Na\u0026thinsp;+\u0026thinsp;influx. Mutations in GPD1L disrupt Na\u0026thinsp;+\u0026thinsp;channel transport, leading to conditions such as Brugada syndrome, sudden infant death syndrome, and various inherited arrhythmia syndromes\u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. While previous research on GPD1L has been largely concentrated in the context of arrhythmias, emerging evidence has implicated its involvement in malignancies, including head and neck squamous cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, oropharyngeal cancer\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, lung cancer\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and renal cancer\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, its role and underlying mechanisms in CRC remain unexplored.\u003c/p\u003e\u003cp\u003eIn our research, a thorough analysis integrating transcriptomic and proteomic from both our cohort and public databases identified GPD1L as a promising biomarker and therapeutic target for CRC. Subsequent investigations assessed GPD1L's influence on CRC cell biology, uncovering pivotal downstream genes and associated molecular pathways, thereby offering novel insights into diagnostic and therapeutic strategies for CRC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eTissue samples\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTissue samples for this study were obtained from primary cancer tissues and adjacent non-cancerous tissues of CRC patients who underwent surgical resection. Wayen Biotechnologies (Shanghai, China) executed the label free proteomic analysis based on the tumor tissues and normal tissues from 10 patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePublic database\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRNAseq data of the TCGA-COAD (Colon adenocarcinoma) and TCGA-READ (Rectum adenocarcinoma) projects were retrieved from the TCGA database. Following the exclusion of samples lacking clinical data, 644 CRC tissue samples and 51 adjacent non-cancerous tissue samples were included, accompanied by relevant clinicopathological data. Other Expression data and clinical follow-up matrices were sourced from the Gene Expression Omnibus database.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell lines\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman CRC cell lines, including SW480 and HCT8, were sourced from the Shanghai Cell Bank of the Chinese Academy of Sciences. All cells had recently been authenticated using the short tandem repeat method and tested for mycoplasma contamination. The last test conducted in September 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIHC staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParaffin sections were immersed in xylene for 10 minutes, followed by gradient dewaxing through a series of ethanol concentrations. Endogenous peroxidase activity was reduced by blocking with hydrogen peroxide for 15 minutes, after which sections were rinsed in PBS. Following antigen retrieval, 5% goat serum was applied for blocking. The blocking solution was then removed, and the primary antibody GPD1L (1:100, Invitrogen, Cat.No.PA5-24216) was applied, with incubation at 37\u0026deg;C for 2 hours or at 4\u0026deg;C overnight. After incubation, the sections were treated with a biotin-labeled secondary antibody. Results were interpreted by two independent pathologists, assessing both staining intensity and distribution. Staining intensity was scored as follows: 0 (negative), 1 (weak positive), 2 (moderate positive), and 3 (strong positive). The staining distribution was graded on a scale of 0 to 4, corresponding to 0\u0026ndash;5%, 6%-25%, 26%-50%, 51%-75%, and 76%-100%, respectively. The final score was calculated by multiplying the intensity score by the distribution score.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern blotting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProtein extraction was performed using Western and IP cell lysis buffer (Beyotime Biotechnology), and concentrations were measured via the BCA assay kit (Solarbio). Protein samples were prepared with 5\u0026times;SDS Loading Buffer at a 4:1 ratio, followed by denaturation at 100\u0026deg;C for 5 minutes. Proteins were then separated by SDS-PAGE and transferred onto a 0.45 \u0026micro;m nitrocellulose membrane at a constant 200 mA. Blocking was achieved with TBST containing 5% skim milk. Primary antibodies were diluted according to manufacturer guidelines and incubated overnight at 4\u0026deg;C on a horizontal shaker. Secondary antibody incubation proceeded for 2 hours at room temperature under similar conditions. Protein detection was conducted using an automated exposure system as per the reagent instructions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLentivirus transfection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe GPD1L overexpression and knockdown lentivirus were constructed by Shanghai Genechem Medical Technology. GFP expression became visible 24\u0026ndash;48 hours post-infection under a fluorescence microscope. At this stage, Puromycin was introduced during subculture to isolate stably transfected cells. The shRNA interference sequences were as followed.\u003c/p\u003e\u003cp\u003eshGPD1L-1: 5\u0026rsquo;-GCTTAAGAACATCGTAGCTGT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eshGPD1L-2: 5\u0026rsquo;-GGAAGACCATTGAAGAGTTGG-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eshNC: 5\u0026rsquo;-TTCTCCGAACGTGTCACGT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cb\u003eRT-qPCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was isolated via the Trizol method and subsequently reverse transcribed using the FastKing-RT SuperMix kit (TIANGEN, Cat.No.KR118). The RT-qPCR was carried out utilizing the iTaq Universal SYBR\u0026reg; Green SuperMix (2\u0026times;) system (Bio-Rad, Cat.No.1725122). Target gene mRNA expression levels were determined using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The primer sequences are as followed:\u003c/p\u003e\u003cp\u003eGPD1L-F:5\u0026rsquo;-GCCAAGTGTCTACAGCCACCTTC-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eGPD1L-R:5\u0026rsquo;-CCCATTCAGCATCTCCTTCTCCAAC-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eGAPDH-F:5\u0026rsquo;-CCCCGGTTTCTATAAATTGAGC-3\u0026rsquo;\u003c/p\u003e\u003cp\u003eGAPDH-R:5\u0026rsquo;-CACCTTCCCCATGGTGTCT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cb\u003eCCK-8 cell proliferation assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe cell suspension was prepared at a concentration of 1\u0026times;10⁴ cells/mL and seeded into 96-well plates. At 24-hour intervals, 90 \u0026micro;L of complete medium and 10 \u0026micro;L of CCK-8 reagent were introduced. Following a 2-hour incubation in a 37\u0026deg;C, 5% CO₂ atmosphere, absorbance was recorded at 450 nm to assess cell viability, and a proliferation curve was generated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColony formation assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCell suspensions were adjusted to a concentration of 1*10\u003csup\u003e4\u003c/sup\u003e/mL and seeded into 6-well plates at the appropriate volume. Culturing was halted once most colonies reached 50\u0026ndash;100 cells. Cells were then fixed with 1 mL of 4% paraformaldehyde for 15 minutes, followed by staining with 1 mL of 0.1% crystal violet for an additional 15 minutes, and air-dried at room temperature. After drying, plates were photographed against a clean white background, and the number of colonies was quantified using ImageJ software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWound healing assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn appropriate volume of cell suspension was inoculated into a 6-well plate. Once cell confluence reached approximately 90\u0026ndash;100%, a longitudinal scratch was made through the center of the culture well, perpendicular to the marked line. Following this, 2mL of low-serum medium (\u0026lt;\u0026thinsp;1% FBS) was added to sustain culturing. Cell migration was documented at the same site using an inverted microscope, accompanied by image capture. The cell area within the scratch at various time points was measured using ImageJ software, and the wound healing rate was calculated for subsequent statistical analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranswell assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe cell density was adjusted to 5*10⁵/mL, and 200 \u0026micro;L of the cell suspension was added to the upper compartment of the Transwell chamber, while 600 \u0026micro;L of culture medium containing 20% FBS was placed in the lower compartment. The culture plate was incubated at 37\u0026deg;C in a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere. After incubation, the chamber was removed, fixed with paraformaldehyde, and stained with 0.1% crystal violet solution. Cells on the upper side of the microporous membrane were carefully removed with a cotton swab. Images were captured using an inverted microscope, and three random fields (100\u0026times; magnification) were counted per sample. The number of cells migrating through the membrane was quantified using ImageJ software. For the invasion assay, the upper surface of the membrane in the Transwell chamber was coated with Matrigel matrix, while the remaining procedures followed those of the migration assay.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscriptome sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe RNA sequencing procedure encompassed sample analysis, library preparation, quality control, and sequencing, conducted by Beijing Biomarker Technologies Co., Ltd. Qualified RNA samples were subjected to library construction, following these procedures: (1) mRNA was isolated using Oligo(dT)-attached magnetic beads. (2) The isolated mRNA was subsequently fragmented randomly in a fragmentation buffer. (3) First-strand cDNA synthesis was performed using the fragmented mRNA as a template and random hexamers as primers. This was followed by second-strand cDNA synthesis, which involved the addition of PCR buffer, dNTPs, RNase H, and DNA polymerase I. The cDNA was then purified using AMPure XP beads. (4) The resulting double-stranded cDNA underwent end repair, with the addition of adenosine at the ends and ligation to adapters. AMPure XP beads were utilized to select fragments within the size range of 300\u0026ndash;400 base pairs. (5) The cDNA library was constructed through several rounds of PCR amplification of the cDNA fragments obtained in step 4. The qualified library was pooled based on pre-designed target data volume and then sequenced on Illumina sequencing platform. After quality control of sequencing data, Clean Data were obtained. The clean data were mapped to the reference genome using HISAT2 to determine the read locations on the reference genome (Homo_sapiens.GRCh38_release95.genome.fa) and to extract characteristic information of the sequenced samples. Based on the reads aligned to the reference genome, transcripts were assembled using StringTie to obtain transcript information for each sample. The number of reads corresponding to each transcript was quantified, followed by expression analyses. Differential expression analysis is processed by DESeq2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCellular iron content and redox parameter detection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCellular iron content (Solarbio, Cat. No. BC5315), intracellular reduced glutathione (GSH) (Solarbio, Cat. No. BC1175), oxidized glutathione (GSSG) (Solarbio, Cat. No. BC1185), glutathione peroxidase (GSH-Px/GPX) activity (Solarbio, Cat. No. BC1195), malondialdehyde (MDA) levels (Solarbio, Cat. No. BC0025), and thioredoxin reductase (TrxR) activity (Solarbio, Cat. No. BC1155) were quantified according to the manufacturer's protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA interference\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUpon reaching 50%-60% cell confluence, siRNA or plasmid and 10\u0026micro;L Lipo2000 should be diluted in 250\u0026micro;L Opti-MEM, mixed gently, and incubated at room temperature for 5 minutes. The resulting complex was then transferred into a 6-well plate and mixed by gentle agitation. After 24 to 48 hours, transfection efficiency can be assessed using Western blot, and additional cellular or molecular biology assays may be conducted as required.\u003c/p\u003e\u003cp\u003esiNC 5\u0026rsquo;-UUCUCCGAACGUGUCACGUTT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003esiATF3-1 5\u0026rsquo;-GAGGCGACGAGAAAGAAAUTT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003esiATF3-2 5\u0026rsquo;-GCUCAGAUUGAGGAGCUCATT-3\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cb\u003eMouse xenograft tumor model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSix-week-old NOD/SCID mice were used for mouse xenograft tumor model. Prior to tumor implantation, each animal underwent a thorough examination to verify health status and ensure the absence of disease. Subsequently, SW480 cells, at a concentration of 5\u0026times;10^6, were subcutaneously injected into the nude mice. After a period of four weeks, the animals were euthanized, and the tumors were excised for measurement of their volumes and weights.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnrichment Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe R software package ClusterProfiler (version 3.6.3) was employed to conduct gene set enrichment analysis (GSEA) in conjunction with assessments of Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and HALLMARK. The GO analysis included the examination of cellular components (CC), molecular functions (MF), and biological processes (BP). GSEA serves as a computational approach to assess the statistical significance and consistency of differences between two biological conditions based on predefined gene sets. The adjusted p-value and normalized enrichment score (NES) were utilized to identify enriched pathways within each phenotype. Gene sets were considered significantly enriched if they met the criteria of an adjusted p-value of less than 0.05 and a false discovery rate (FDR) of less than 0.25.16. Statistical analysis\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor numerical variables, the t-test was applied to assess differences between two groups when the data followed a normal distribution and passed the homogeneity of variance test, while one-way ANOVA was employed for comparisons across three or more groups. If the data adhered to a normal distribution but failed the variance homogeneity test, the Welch t-test was utilized for two-group comparisons, and the Welch one-way ANOVA for three or more groups. When the data deviated from a normal distribution, the Wilcoxon test was used for two-group comparisons, and the Kruskal-Wallis test for three or more groups. Categorical variables were analyzed using the chi-square test when the theoretical frequency exceeded 5 and the sample size was \u0026ge;\u0026thinsp;40. For theoretical frequencies between 1 and 5 with sample sizes\u0026thinsp;\u0026ge;\u0026thinsp;40, the continuity correction chi-square test (Yates' correction) was applied. When the theoretical frequency was \u0026lt;\u0026thinsp;1 or the sample size was \u0026lt;\u0026thinsp;40, the Fisher exact test was employed. Patient survival evaluations were conducted employing KM methodology and log-rank statistical testing. The correlation between clinical-pathological parameters and patient outcomes underwent assessment via univariate and multivariate Cox regression models. Predictive factors yielding p-values below 0.1 in the univariate evaluation were selected for subsequent multivariate examination. Following this, multivariate Cox analysis was utilized to identify independent prognostic markers. A significance threshold of \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 was set for all statistical analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDifferential expression of proteins in tumor and normal tissues of CRC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine protein expression patterns in CRC, we gathered 10 matched sets of colorectal cancer specimens and neighboring healthy tissue samples for comparative protein analysis via label free proteomics. Setting the differential expression threshold at LogFC(fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1 and statistical significance at P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, our analysis revealed 278 proteins showing elevated expression and 485 proteins exhibiting reduced expression within the cancer specimens among the measured proteins (Figure. 1A, B and Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eTo further explore proteins that may play significant roles in the development and progression of CRC, we downloaded gene expression and prognostic data of CRC patients from TCGA database. Survival analysis was performed to identify genes significantly associated with overall survival (OS). The results revealed 1,705 genes significantly correlated with OS in the TCGA CRC cohort, including 631 risk genes (HR\u0026thinsp;\u0026gt;\u0026thinsp;1) and 1,074 protective genes (HR\u0026thinsp;\u0026lt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). By intersecting the differentially expressed proteins identified through proteomics with these prognostic genes, we identified 4 upregulated genes as risk genes and 24 downregulated genes as protective genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Among these genes, GPD1L captured our attention due to its high differential expression and strong prognostic relevance (Supplementary Table\u0026nbsp;2). However, its functional role in the development and progression of CRC remains incompletely understood, warranting further in-depth investigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGPD1L expression was reduced in CRC compared to normal tissues\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUtilizing TCGA database resources, we investigated GPD1L gene expression in CRC cohort. We confirmed that GPD1L expression in CRC tissues was markedly lower than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Comparative analysis of paired transcriptome samples further demonstrated a consistent downregulation of GPD1L in CRC tissues relative to their matched normal counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). ROC curve analysis yielded an AUC of 0.969 (95% CI\u0026thinsp;=\u0026thinsp;0.954\u0026ndash;0.983), suggesting strong discriminatory capability between tumor and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Additional validation of GPD1L\u0026rsquo;s downregulated expression in CRC came from CPTAC and GEO datasets (GSE89076, GSE110223, GSE113513, and GSE22598) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u0026ndash;K). Western blot analysis of GPD1L expression in frozen tissues from CRC tissues and their paired normal colorectal mucosa revealed a marked reduction of GPD1L protein levels in tumor tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL, M). IHC staining of paraffin-embedded sections from 73 CRC tissues and their paired normal tissues confirmed that GPD1L protein levels were also substantially lower in tumor tissues than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.N, O). Collectively, these results provided strong evidence that GPD1L expression was significantly lower in CRC tissues compared to normal tissues.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLow GPD1L expression was linked to an advanced pathological stage and poor prognosis in CRC patients\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe TCGA CRC cohort was stratified into low (n\u0026thinsp;=\u0026thinsp;322) and high expression (n\u0026thinsp;=\u0026thinsp;322) groups based on the median mRNA expression level of GPD1L, and its association with clinicopathological features in CRC patients was examined. GPD1L expression exhibited a statistically significant correlation with T stage, N stage, M stage, and pathologic overall stage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, based on the median IHC staining score, 73 patients were categorized into a GPD1L low-expression group (n\u0026thinsp;=\u0026thinsp;30) and a high-expression group (n\u0026thinsp;=\u0026thinsp;43), followed by an analysis of the association between GPD1L protein levels and clinicopathological parameters., significant associations were identified between GPD1L expression and T stage, N stage, M stage, and AJCC stage, with statistically significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe correlation of clinicopathological characteristics and GPD1L expression in TCGA cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow expression of GPD1L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh expression of GPD1L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;= 65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 (33.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222 (34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe correlation of clinicopathological characteristics and GPD1L expression in IHC cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (24.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u0026amp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (57.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJCC, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u0026amp;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u0026amp;IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (24.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe evaluated GPD1L\u0026rsquo;s prognostic significance in predicting OS, DSS, and PFI outcomes across CRC cases. KM survival assessment using TCGA data demonstrated enhanced OS, DSS, and PFI in patients expressing higher GPD1L levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eP\u0026ndash;R). Through univariate and multivariate examinations, lower GPD1L emerged as an autonomous risk indicator for CRC patients regarding OS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and DSS (Supplementary Table\u0026nbsp;3), as well as PFI (Supplementary Table\u0026nbsp;4). External validation via GEO datasets (GSE14333, GSE17536) showed associations between heightened GPD1L levels and better DFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS, T). These findings suggest that enhanced low GPD1L expression indicates poorer CRC patient outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox analyses of factors affecting the OS of CRC patients in the TCGA database\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal(N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic T stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.468 (1.327\u0026ndash;4.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.285 (1.035\u0026ndash;5.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic N stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u0026amp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.627 (1.831\u0026ndash;3.769)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.403 (0.153\u0026ndash;1.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic M stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.989 (2.684\u0026ndash;5.929)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.314 (1.433\u0026ndash;3.738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.988 (2.042\u0026ndash;4.372)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.242 (1.794\u0026ndash;15.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.054 (0.744\u0026ndash;1.491)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;= 65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.939 (1.320\u0026ndash;2.849)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.617 (1.679\u0026ndash;4.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPD1L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.560 (0.392\u0026ndash;0.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.633 (0.424\u0026ndash;0.946)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGPD1L affected CRC cell biological function\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, shRNAs were designed to target GPD1L expression in cells. These shRNAs were transfected into SW480 and HCT8 cell lines to generate stable GPD1L knockdown models. Results from qPCR and Western blot analyses confirmed a significant reduction in both mRNA and protein levels of GPD1L in all treated cells. Additionally, GPD1L-overexpressing SW480 and HCT8 cell lines were established via transfection with GPD1L overexpression plasmids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). The CCK-8 assay demonstrated that GPD1L overexpression markedly suppressed the proliferation of SW480 and HCT8 cells. Conversely, GPD1L knockdown significantly promoted cell proliferation in both SW480 and HCT8 lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The colony formation assay further confirmed that increased GPD1L expression significantly reduced colony formation, while decreased GPD1L expression led to a notable enhancement in clonogenic capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). In the wound healing assay, overexpression of GPD1L resulted in a significantly slower scratch healing rate compared to the control group, whereas GPD1L knockdown accelerated the healing process (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, G). Migration assays revealed that GPD1L overexpression significantly impaired the migratory ability of SW480 and HCT8 cells, while its knockdown enhanced cell migration. Similarly, invasion assays showed that GPD1L overexpression significantly reduced invasive capabilities, whereas GPD1L knockdown had the opposite effect, markedly increasing the invasion potential of both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, I). To evaluate GPD1L\u0026rsquo;s impact on colon cancer cells within living organisms, we developed tumor xenograft models. Following the implantation of GPD1L-depleted cells (n\u0026thinsp;=\u0026thinsp;5), we observed increased tumor dimensions and mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ, K). Furthermore, Ki-67 expression levels were higher in shGPD1L cohorts relative to shNC controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe potential biological mechanism of GPD1L involved in the malignant progression of CRC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate GPD1L\u0026rsquo;s biological functions in CRC, we determined genes showing a significant correlation with GPD1L, illustrated in the heatmap visualization (top25) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, the following were found to be predominantly linked to co-expressed genes that exhibited a correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.5 with GPD1L, as indicated by the GO term annotation: purine-containing compound metabolic process, ribose phosphate metabolic process, purine nucleotide metabolic process, ribonucleotide metabolic process, mitochondrial matrix, magnesium ion binding and oxidoreductase activity. A review of the KEGG pathways showed the enrichment of the majority of the GPD1L-associated genes in the following: Phosphatidylinositol signaling system, Inositol phosphate metabolism, Propanoate metabolism and the Citrate cycle (TCA cycle) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, 1250 DEGs were found between the low- and high-expression groups with 95 and 1155 up- and down-regulated genes, respectively, in the GPD1L high-expression subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Subsequently, we conducted GO and KEGG pathway analyses. A bubble map revealed the enrichment of upregulated DEGs in the glucuronate metabolic process, uronic acid metabolic process, cellular glucuronidation, glucuronosyltransferase activity, sodium ion transmembrane transporter activity, secondary active transmembrane transporter activity for GO pathway analysis, and Porphyrin metabolism, Pentose and glucuronate interconversions, Ascorbate and aldarate metabolism for KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Additionally, GSEA results demonstrated the negative correlation of GPD1L expression with the IL6-JAK-STAT3 Signaling, TNFα Signaling Via NF-Κb, angiogenesis, apical junction, KRAs Signaling, epithelial mesenchymal transition in Hallmark pathways, cell fate commitment, external encapsulating structure organization, glycosaminoglycan binding, serine hydrolase activity, collagen containing extracellular matrix, extracellular matrix structural constituent in GO and focal adhesion, hedgehog signaling pathway, basal cell carcinoma, ECM receptor interaction, cell adhesion molecules cams (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). Overall, these findings point to the potential involvement of GPD1L in the CRC malignant progression. Based on the aforementioned bioinformatics analysis, we hypothesized that GPD1L, as a biochemical enzyme, might play a significant role in tumor metabolism, metal ion transport and oxidoreductase activity, thereby determining cell fate and malignant biological phenotypes such as invasion and migration.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGPD1L affected ferroptosis by affecting the expression level of ATF3.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the biological functions of GPD1L, we conducted transcriptome sequencing. This approach aims to elucidate the molecular mechanisms and downstream pathways regulated by GPD1L, providing deeper insights into its role in CRC progression. Transcriptome sequencing was performed on the stably transfected human CRC cell line SW480, with GPD1L overexpression (GPD1L-OE) and its control (Vector), as well as on the GPD1L knockdown cell line (shGPD1L) and its control (shNC). Differential gene expression was screened using Fold-change\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the criteria. In the GPD1L overexpression group, 347 genes exhibited significant expression changes compared to the control, with 47 up-regulated and 300 down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the knockdown group, 209 genes showed significant changes, with 57 up-regulated and 152down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This study identified 4 molecules with expression positively correlated with GPD1L by intersecting the upregulated molecules in the GPD1L overexpression group and the downregulated molecules in the GPD1L knockdown group. Similarly, intersecting the downregulated molecules in the GPD1L overexpression group with the upregulated molecules in the GPD1L knockdown group revealed 4 molecules negatively correlated with GPD1L expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong these, ATF3, a ferroptosis-inducing factor, has captured our attention. Ferroptosis is an iron-dependent, non-apoptotic form of cell death that differs from traditional cell death mechanisms such as apoptosis, necrosis, and autophagy. It is characterized by unique biochemical features and molecular mechanisms, involving dysregulated iron metabolism, lipid peroxidation, glutathione depletion, and GPX4 inactivation, all of which contribute to redox homeostasis imbalance. This aligns closely with our preliminary bioinformatics analysis. Therefore, we hypothesize that GPD1L may regulate ferroptosis through its modulation of ATF3. To validate this hypothesis, we examined the correlation between GPD1L and ATF3 expression in CRC samples. The expression of ATF3 and GPD1L exhibits a strong correlation, suggesting a potential regulatory relationship between these two genes. This consistency in expression patterns further supports our hypothesis that GPD1L may influence ferroptosis, through its interaction with ATF3(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eWe investigated the regulatory impact of GPD1L expression on ATF3 and key ferroptosis-associated markers at the molecular level. Overexpression of GPD1L elevated the levels of ferroptosis-promoting molecules, including ATF3, ACSL4, and TP53, while reducing the expression of the ferroptosis-inhibitor GPX4. In contrast, GPD1L knockdown led to decreased levels of ATF3, ACSL4, and TP53, alongside an increase in GPX4 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Furthermore, siRNAs were designed to interfere ATF3 expression in cells for further rescue experiments. Our experiments validated that the suppression of ferroptosis in CRC cells, induced by reduced GPD1L levels, was mediated through decreased ATF3 expression. This effect was reversible by upregulating ATF3 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-I).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGPD1L induced ferroptosis in CRC.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIntracellular iron levels serve as an indicator of cellular ferroptosis, with increased iron content observed during ferroptosis. By measuring changes in intracellular iron following ferroptosis induced by GPD1L expression modulation, the influence of GPD1L on ferroptosis can be assessed. Experimental data demonstrated that erastin-induced ferroptosis in CRC cells resulted in elevated intracellular iron levels in SW480 and HCT8 cell lines overexpressing GPD1L, compared to the control group. Conversely, GPD1L knockdown in SW480 and HCT8 cell lines led to reduced intracellular iron content relative to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGPX activity serves as a reliable indicator of intracellular ferroptosis, with decreased GPX activity correlating with elevated ferroptosis levels. Following erastin-induced ferroptosis in CRC cells, GPD1L-overexpressing SW480 and HCT8 cell lines exhibited lower intracellular GPX activity compared to the control group. In contrast, GPD1L-knockdown SW480 and HCT8 cell lines showed higher GPX activity than the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGSH levels are inversely associated with ferroptosis. Monitoring the intracellular GSH content in GPD1L-regulated cells allows assessment of GPD1L's impact on ferroptosis. Experimental data revealed that, following erastin-induced ferroptosis in CRC cells, intracellular GSH levels were reduced in GPD1L-overexpressing SW480 and HCT8 cell lines compared to the control group. Conversely, GSH levels were elevated in GPD1L-knockdown SW480 and HCT8 cells relative to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eGSSG levels were also a marker for ferroptosis, with elevated intracellular oxidative stress and GSSG content indicating increased ferroptosis. The experimental results demonstrated that, following erastin-induced ferroptosis in CRC cells, GPD1L-overexpressing SW480 and HCT8 cell lines exhibited higher intracellular GSSG levels compared to the control group, whereas GPD1L-knockdown SW480 and HCT8 cell lines showed lower intracellular GSSG levels relative to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eOxygen free radicals produced during cellular metabolic processes can react with unsaturated fatty acids in lipids, leading to lipid peroxidation and the formation of various complex compounds, with MDA being a key marker. Measuring MDA levels provides an accurate indication of lipid peroxidation in tissue cells. As ferroptosis increases, lipid peroxidation intensifies, making MDA detection a reliable indirect measure of ferroptosis. Following erastin-induced ferroptosis in CRC cells, results indicated that intracellular MDA levels were elevated in SW480 and HCT8 cells overexpressing GPD1L compared to the control group, whereas MDA levels were reduced in GPD1L knockdown cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eThioredoxin reductase (TrxR) shares functional similarities with glutathione reductase, catalyzing the reduction of GSSG to GSH, and plays a central role in the glutathione redox cycle. Reduced TrxR content or activity leads to decreased GSH levels, weakening the cell's ability to counteract oxidative stress. As such, TrxR activity assays serve as indicators of intracellular oxidative stress and ferroptosis. Following erastin-induced ferroptosis in CRC cells, results demonstrated that TrxR activity was lower in GPD1L-overexpressing SW480 and HCT8 cell lines compared to controls, while GPD1L-knockdown SW480 and HCT8 cell lines exhibited higher TrxR activity than their respective controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These results indicate that GPD1L played a role in regulating ferroptosis in colon cancer cells.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC ranks among the most prevalent gastrointestinal malignancies, accounting for nearly 900,000 deaths annually worldwide\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Although early detection, diagnosis, and treatment have significantly lowered CRC mortality, a substantial number of cases, particularly in advanced stages, remain resistant to current therapeutic interventions. Increasing evidence highlights CRC as a highly heterogeneous disease, marked by the gradual accumulation of genetic and epigenetic alterations, with significant inter- and intra-tumor variability. The tumor\u0026rsquo;s protein expression profile dictates its progression, therapeutic response, and prognosis. Advancing the understanding of CRC\u0026rsquo;s molecular landscape is essential for achieving earlier diagnosis and more targeted, effective treatments, ultimately improving patient outcomes.\u003c/p\u003e\u003cp\u003ePrevious research has demonstrated a marked downregulation of GPD1L expression in head and neck squamous cell carcinoma (HNSCC), with low GPD1L levels strongly associated with higher rates of postoperative local recurrence and poor long-term prognosis in HNSCC patients \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Liu \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003ereported that GPD1L expression served as a predictor of lymph node metastasis in oropharyngeal cancer. Fan \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003eidentified GPD1L as a potential diagnostic and prognostic marker for lung cancer, with significantly reduced expression independently correlating with poor long-term survival. Similarly, Liu \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e observed reduced GPD1L levels in renal cancer tissues compared to adjacent normal tissues, noting a positive correlation between GPD1L expression and renal cancer prognosis. These results align with the present study's conclusions regarding CRC. Our study demonstrated that GPD1L expression at both mRNA and protein levels was markedly downregulated in CRC tissues, indicating its potential as a diagnostic marker and its role as a tumor suppressor in CRC pathogenesis. Additionally, GPD1L expression was significantly associated with clinicopathological parameters, including TNM stage, while survival analysis identified GPD1L as an independent prognostic factor for long-term outcomes in CRC. This study provides comprehensive evidence of GPD1L\u0026rsquo;s expression and clinical relevance in CRC, supporting its potential as both a diagnostic and prognostic biomarker for CRC patients.\u003c/p\u003e\u003cp\u003ePrevious studies have suggested that alterations in GPD1L expression influence the biological behavior of both benign and malignant cells. For instance, Hao \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003ereported that GPD1L knockdown stimulated the proliferation and collagen synthesis of atrial fibroblasts, while Liu \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003efound that GPD1L overexpression in renal cancer cells markedly inhibited cell proliferation, migration, and invasion, while promoting apoptosis. However, no studies have previously investigated the role of GPD1L in CRC. The present study confirmed that modulating GPD1L expression significantly impacted the biological behavior of CRC cells. GPD1L functions as a tumor suppressor gene in CRC development, providing cellular and molecular evidence that supports prior clinical findings and establishes a phenotypic foundation for further mechanistic exploration.\u003c/p\u003e\u003cp\u003eIn our study, transcriptome sequencing of stably transfected cells following GPD1L expression modulation revealed that GPD1L significantly influenced the expression of ATF3, a ferroptosis-promoting molecule. Further analysis demonstrated that altering GPD1L expression markedly affected iron content, lipid peroxidation, GSH levels, and the expression of key ferroptosis regulators, including ATF3, ACSL4, TP53, and GPX4, in CRC cells treated with erastin. These results confirm that GPD1L plays a regulatory role in CRC cell ferroptosis. Specifically, GPD1L overexpression promotes ferroptosis, while its knockdown inhibits this process. Complementary experiments modulating ATF3 expression showed that changes in ATF3 levels can reverse GPD1L-induced alterations in ferroptosis-related factors, indicating that ATF3 serves as a critical pathway in the GPD1L-driven regulation of CRC ferroptosis.\u003c/p\u003e\u003cp\u003eATF3 is a stress-responsive transcription factor and a central component of the cellular adaptive response network. It plays a role in processes such as metabolic regulation and tumor immunity by binding to cAMP response elements in target genes, and its involvement in the pathogenesis of various malignancies is well-documented. Recognized for its role in promoting ferroptosis, ATF3 has been extensively studied\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Research indicates that ATF3 expression is reduced in CRC\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments have demonstrated that ATF3 overexpression inhibits CRC cell proliferation, migration, and invasion\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, while also inducing apoptosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Zhao \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e reported that LncRNA DLEU1 suppressed ferroptosis in glioblastoma by binding to ZFP36, which accelerated the degradation of ATF3 mRNA, thereby increasing SLC7A11 expression. Qian \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003eidentified Shikonin as an inhibitor of non-small cell lung cancer proliferation, achieved by upregulating ATF3 expression and inducing ferroptosis via histone acetylation. Wang \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e provided strong evidence that ATF3 enhanced erastin-induced ferroptosis. Shen \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003edemonstrated that PARP inhibitors can enhance anti-tumor immune responses and ferroptosis in CRC through the cGAS pathway and the ATF3/SLC7A11/GPX4 axis. These studies collectively indicate that ATF3 induces ferroptosis. The upstream regulatory mechanisms governing ATF3 expression are complex. As an adaptive response gene, ATF3 can be induced by various stimuli, such as cytokines, pharmacological agents, and stress-related signals, subsequently modulating the expression of target genes\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. However, the interaction between GPD1L and ATF3 has not been previously explored. This study provides the first experimental evidence that GPD1L functions as an upstream regulator of ATF3 expression and regulated ferroptosis in CRC, marking a significant novel contribution.\u003c/p\u003e\u003cp\u003eDespite strengths, this study presents certain limitations and areas for improvement. First, the investigation of GPD1L's role in regulating CRC ferroptosis via ATF3 requires further refinement. Beyond the biochemical assays already conducted, such as measuring cellular iron content and various redox parameters, ferroptosis detection could be enhanced by employing transmission or scanning electron microscopy to capture the distinctive morphological changes in cells and mitochondria associated with ferroptosis. Moreover, the mechanism by which GPD1L modulates ATF3 expression remains unresolved. Further studies are necessary to elucidate whether the regulation is direct or indirect, necessitating deeper mechanistic exploration.\u003c/p\u003e\u003cp\u003eIn conclusion, this study comprehensively analyzed the alterations in GPD1L expression and their clinical significance in CRC, confirming the impact of GPD1L on the biological behavior of CRC cells. Additionally, the underlying mechanism of GPD1L's regulation of CRC cell ferroptosis via ATF3 was thoroughly investigated. These findings suggest that GPD1L could serve as a novel molecular marker and potential therapeutic target for CRC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this study mainly come from public databases: The Gene Expression Omnibus (GEO) database: http://www.ncbi.nlm.nih.gov/geo; The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov/. Other original data presented in the study are included in the article/supplementary materials, the RNA sequencing data relating to GPD1L transcriptomics was uploaded to GEO database (GSE296448), and further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Postdoctoral Scientific Research Startup Fund in 2024 of the First Affiliated Hospital of Zhengzhou University. Project Code: 72410.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.J.Y. contributed to the conception and design of the study. C.J.Y and Z.L. extracted and analyzed the data. C.J.Y and Z.H.Z conducted experiments and drafted the manuscript. L.G. completed critical review and funding support. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the First Affiliated Hospital of Zhengzhou University.\u0026nbsp;The animal study was reviewed and approved by the First Affiliated Hospital of Zhengzhou University. Written informed consent was obtained from the individuals for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKeum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies[J]. Nat Rev Gastroenterol Hepatol. 2019;16(12):713\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41575-019-0189-8\u003c/span\u003e\u003cspan address=\"10.1038/s41575-019-0189-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShao Y, Xu K, Zheng X, Zhou B, Zhang X, Wang L, et al. 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ATF3 transcription factor and its emerging roles in immunity and cancer[J]. J Mol Med (Berl). 2009;87(11):1053\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00109-009-0520-x\u003c/span\u003e\u003cspan address=\"10.1007/s00109-009-0520-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glycerol-3-Phosphate Dehydrogenase 1-Like, colorectal cancer, ferroptosis, prognosis, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7120750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7120750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) continues to be a prevalent malignancy, posing a significant risk to human health. The involvement of Glycerol-3-phosphate dehydrogenase 1-like (GPD1L) in CRC development was suggested by our analysis of clinical samples. However, the role of GPD1L in CRC remains unclear. This study seeks to elucidate the clinical relevance, biological function, and potential molecular mechanisms of GPD1L in CRC. Our results demonstrated that GPD1L expression in CRC tissues was notably lower compared to adjacent normal tissues. This low expression correlated with a poorer prognosis for CRC patients. GPD1L overexpression impeded CRC cell proliferation and migration. Moreover, knockdown of GPD1L could reduce the iron content and lipid oxidation level, increase the antioxidant capacity of cells, and weaken the ferroptosis of CRC cells. Mechanistically, GPD1L affected ferroptosis by affecting the expression level of ATF3, and finally led to the change of the biological behavior of CRC cells. In summary, GPD1L functions as a tumor suppressor, primarily by promoting ferroptosis through ATF3 and affects the malignant phenotype and biological behavior of CRC. This role established GPD1L as a promising prognostic biomarker and a potential therapeutic target for patients with CRC.\u003c/p\u003e","manuscriptTitle":"Glycerol-3-Phosphate Dehydrogenase 1-Like serves as a tumor suppressor in colorectal cancer through ATF3-mediated ferroptosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:56:41","doi":"10.21203/rs.3.rs-7120750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56e4a4e5-3b43-4cff-b296-b382346b1371","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T03:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 05:56:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7120750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7120750","identity":"rs-7120750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}