A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer

A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer | 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 Article A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer Xian-wen Guo, Rong-e Lei, Jiao Li, Liqi Shen, Zhen Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5382756/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 Background GCSH, a gene associated with cuproptosis, has been implicated in various cancers, although its role remains incompletely understood. This study aims to conduct a comprehensive analysis of GCSH across multiple cancer types to elucidate its role in tumorigenesis. Methods GCSH expression was analyzed in 33 cancer types using data from TCGA database. Associations with the tumor microenvironment and prognostic value were evaluated. scRNA-seq data from colorectal cancer (CRC) was used to assess GCSH expression in different cell types. Clinical CRC tissues, blood samples, and cell lines were utilized for validation. Functional assays and drug sensitivity tests were performed to further elucidate the role of GCSH. Results GCSH expression varied among different cancers, with notably higher levels in CRC. GCSH demonstrated significant correlations with 22 types of immune cells across the 33 cancers. Generally, GCSH showed a negative correlation with immune scores and immune checkpoint genes. Prognostic analysis revealed that GCSH was associated with outcomes in adrenocortical carcinoma, hepatocellular carcinoma, and stomach adenocarcinoma, although external cohort results did not consistently support these findings. Validation in clinical samples and cell lines confirmed elevated GCSH in CRC. scRNA-seq data indicated higher GCSH expression in both cancerous and immune cells within tumor tissues compared to normal tissues. Functional and pathway analyses in CRC identified key biological roles for GCSH, and a drug sensitivity to GCSH was identified. Conclusions GCSH exerts multifaceted roles in specific cancers and is significantly associated with immune cells and immune checkpoint genes. The study identifies the biological functions of GCSH in CRC and suggests potential drug sensitivities. Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Health care Physical sciences/Mathematics and computing GCSH pan-cancer colorectal cancer immune analysis function analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Copper is an essential trace element that acts as a cofactor for over thirty enzymes involved in numerous physiological processes [1]. Recent research indicates that cells strictly regulate copper concentration, as elevated levels can be cytotoxic and even death [2]. Dysregulated copper homeostasis has been implicated in tumor development and metastasis [3]. Cuproptosis, a novel form of programmed cell death driven by copper toxicity, disrupts mitochondrial function, leading to cell death [4]. Altered copper metabolism is a common feature in cancer cells, making copper a potential target for cancer therapies [5]. Elevated copper levels aid cancer cell proliferation, angiogenesis, and metastasis [6]. Cuproptosis-related genes significantly influence cancer progression by modulating copper levels and mitochondrial function [7]. To date, several such genes have been identified, including FDX1, SLC31A1, LIPT1, LIAS, DLD, DBT, GCSH, DLST, DLAT, PDHA1, PDHB, and ATP7A [8]. While some of these genes have been implicated in various cancers, others remain underexplored. For instance, SLC31A1 is a predictive biomarker in gynecological cancers [9]. DLD serves as a marker for early diagnosis and immunotherapy in osteomyelitis [10], and FDX1 is a potential diagnostic biomarker and therapeutic target for ovarian aging [11]. However, the roles of many cuproptosis-related genes in cancers remain to be fully elucidated. GCSH (Glycine Cleavage System Protein H) is a critical cuproptosis-related gene [5]. It plays a vital role in the glycine cleavage system [12]. Defects in GCSH are linked to nonketotic hyperglycinemia, lipoate deficiency [13] and rheumatoid arthritis [14]. Elevated levels of GCSH have been observed in prostate adenocarcinoma [15], gliomas [16], and sarcoma [17]. However, its role in many other cancers remains under investigation. This study undertakes a comprehensive analysis of GCSH using multi-omics data in pan-cancer contexts and clinical samples, aiming to provide key insights into the effect of GCSH in tumorigenesis. Material and Methods Collection of pan-cancer datasets The multi-omics data of pan-cancer cohorts and corresponding clinical data were sourced from the TCGAplot R package [18], which aggregates data from The Cancer Genome Atlas (TCGA) encompassing 33 types of cancers. The data were normalized to transcripts per million (log2(TPM + 1)) values. Additionally, two Gene Expression Omnibus (GEO) datasets comprising 223 samples of primary colorectal cancer (CRC) and normal tissues—GSE106582 (n = 194) and GSE146889 (n = 176)—were retrieved for analysis. To evaluate survival outcomes, three GEO datasets with follow-up data were utilized, including GSE12417 (adrenocortical carcinoma, ACC, n = 163), GSE76427 (hepatocellular carcinoma, HCC, n = 167), and GSE84437 (stomach adenocarcinoma, STAD, n = 433). All gene datasets underwent preprocessing, including background adjustment via the Robust Multi-array Average (RMA) algorithm. Immunocytochemistry images of GCSH in CRC were obtained from ( http://www.proteinatlas.org/ ). Tumor microenvironment and immune checkpoint genes analysis The CIBERSORT algorithm [19] was employed to quantify the proportion of immune infiltrating cells within the tumor microenvironment (TME) across different cancers, generating profiles of 22 human immune cell types based on gene expression data. Tumor immune scores, encompassing immune score, stromal score, and ESTIMATE score, were computed using the ESTIMATE algorithm. For immune checkpoint analysis, eight critical immune checkpoint genes were assessed: CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT [20]. ScRNA-seq analysis for CRC dataset The scRNA-seq dataset of GSE132465 and annotation data were downloaded from GEO database, which included data of 23 primary CRC and 10 matched normal mucosa samples. The Seurat 4.2.0 R package was carried out to the processing of the scRNA-seq data according to the standard workflow. Initially, raw data is imported and converted into a Seurat object. Quality control is performed to filter out low-quality cells and genes. The data is then normalized and scaled. Highly variable genes are identified, followed by principal component analysis (PCA) to reduce dimensionality. Clustering is conducted based on significant principal components, and cluster biomarkers are identified. Finally, data visualization method of t-SNE is applied to visualize the clusters, and further downstream analyses can be performed. The markers used for cell identity were obtained from the ScType package [21]. Clinical CRC tissue and blood samples collection From January 2021 to February 2022, fifteen colorectal cancer (CRC) tissues and paired adjacent non-tumorous tissues were collected at our hospital. The inclusion criteria ensured that CRC patients had not received any treatments prior to surgery and were free from immune diseases, inflammatory diseases, or severe major organ dysfunction. Additionally, blood samples were obtained from 30 CRC patients and 30 healthy controls during the same period. A 10 mL peripheral venous blood sample was collected in an EDTA anticoagulant tube for peripheral blood mononuclear cell (PBMC) separation. The PBMCs were subsequently stored at -80°C until further processing. This study was approved by the hospital’s ethics committee, and written informed consent was obtained from each patient. The clinical features of the CRC samples are detailed in Table S1-S2. CRC cell lines culture Three CRC cell lines (HCT116, LoVo, and HT-29) and one human colon mucosal epithelial cell line (NCM460) were procured from the Chinese Academy of Sciences (Shanghai, China). HCT116, LoVo, and HT-29 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) with GlutaMAX (Gibco), supplemented with 10% fetal bovine serum (FBS) and 1% streptomycin/penicillin. NCM460 cells were cultured in phenol red–free RPMI 1640 (Gibco), supplemented with 10% FBS and 1% streptomycin/penicillin. RNA extraction and real‑time quantitative polymerase chain reaction (RT‑qPCR) Using TRIzol reagent, total RNA was extracted and quantified using a NanoDrop spectrophotometer. Using a cDNA reverse transcription reagent, total RNA was reverse-transcribed. In a 7500 real-time PCR system with SYBR green, PCR amplification was performed at 95°C for 30 s, followed by 50 cycles of 95°C for 10 s and 60°C for 34 s. All primers were synthesized by company Jinweizhi (Tianjin, China). GAPDH primers Forward: 5′-TCA GCA ATG CCT CCT GCA C-3′, Reverse: 5′-TCT, GGG, TGG, CAG, TGA, TGG, C. SLC31A1 primers: 5′-3′Forward: GGG GAT GAG CTATATGGACTCC; Reverse: TCACCAA ACC GGAAAACAGTAG. The expression of GAPDH mRNA was used to normalize the expression of the target gene in each sample. Analyzing relative mRNA expression levels using the 2 − ΔΔCt method. Gene functional enrichment analysis for GCSH in CRC To identify differentially expressed genes (DEGs) associated with high and low expression of GCSH, we utilized the R “limma” package. The biological processes (BP) enrichment and KEGG pathway enrichment analyses for these DEGs in the TCGA-COAD dataset were performed using the TCGAplot R package. These analyses aimed to elucidate the functional roles of GCSH in CRC. Drug sensitivity analysis To explore potential small-molecule drugs targeting GCSH, RNA expression data and drug sensitivity data for the NCI-60 cell lines were obtained from the “CellMiner” package [22]. We reanalyzed the correlation between GCSH expression and the efficacy of small-molecule drugs. Additionally, the sensitivity of these drugs was compared between high- and low-expression groups of GCSH. Statistical analysis Statistical analyses were carried out using R software (version 4.3.0). Clinical features with a normal distribution were compared using the 2-tailed Student’s t-test, while those with an abnormal distribution were analyzed using the Wilcoxon rank-sum test. Differences in survival between the two risk groups were assessed using the Kaplan-Meier survival curve (KM) and log-rank test. A two-tailed p-value < 0.05 was considered statistically significant. Results Analysis of differential expression of GCSH in pan-cancer We compared the expression of GCSH between tumor and normal tissues across 33 types of cancers using the TCGA dataset. As depicted in Fig. 1 , GCSH expression was significantly higher in tumor tissues compared to normal tissues in colon adenocarcinoma (COAD), bladder urothelial carcinoma (BLCA), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), kidney renal papillary cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), and rectum adenocarcinoma (READ) (P < 0.05). Conversely, GCSH expression was lower in tumor tissues than in normal tissues in breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), sarcoma (SARC), and thyroid carcinoma (THCA) (P < 0.05). No significant differences were observed in the remaining malignancies. These results suggest that GCSH expression is cancer type-dependent, with high expression predominantly in gastrointestinal tumors, particularly CRC. Correlation analysis of GCSH with TME and immune checkpoint genes in pan‑cancer We investigated the correlation of GCSH with the tumor microenvironment (TME) in pan-cancer using the TCGA dataset. The CIBERSORT and ESTIMATE algorithms were employed to determine the 22 types of immune cells and tumor immune scores, respectively. As shown in Fig. 2 A, GCSH was significantly positively correlated with memory B cells, activated dendritic cells, resting NK cells, naive CD4 + T cells, and follicular helper T cells, while negatively correlated with regulatory T cells (Tregs) across all 33 cancer types (P < 0.05). The correlations of GCSH with other immune cells varied across the cancer types. Regarding the tumor immune scores, Fig. 2 B indicates that GCSH was significantly negatively correlated with stromal score, immune score, and ESTIMATE score across the 33 types of cancers. For the correlation of GCSH with eight immune checkpoint genes, we found that, except for uveal melanoma (UVM), GCSH was significantly negatively correlated with almost all immune checkpoint gene markers in the remaining 32 types of cancers. Interestingly, GCSH was positively correlated with SIGLEC15 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), pancreatic adenocarcinoma (PAAD), and thymoma (THYM) (Fig. 2 C). Prognostic value of GCSH in cancer patients across pan-cancer Using the TCGA dataset, we examined the prognostic value of GCSH in cancer patients across pan-cancer using Cox regression analysis. As listed in Fig. 3 A, a significant prognostic value of GCSH was observed only in adrenocortical carcinoma (ACC), liver hepatocellular carcinoma (LIHC), and stomach adenocarcinoma (STAD) (P 0.05). We further validated the association of GCSH with patient survival in ACC (GSE12417, n = 163), LIHC (GSE76427, n = 167), and STAD (GSE84437, n = 433) using external cohorts. Using the median value of GCSH as the cut-off, the Kaplan-Meier plots showed no significant difference in survival time between high and low GCSH expression in patients with ACC, HCC, and STAD (Fig. 3 B-D), suggesting that the prognostic value of GCSH in these patients requires further validation. Validation of GCSH expression in CRC tissues, blood and cells Given that GCSH expression was significantly higher in gastrointestinal tumors, especially CRC, we selected CRC to validate GCSH expression. First, we analyzed GCSH expression in two large CRC datasets (GSE106582, n = 194; GSE146889, n = 176), and the results showed significantly increased GCSH expression in CRC tissues compared to normal tissues (Fig. 4 A-B). The protein localization of GCSH was determined using the IHC method in the HPA database, revealing that GCSH was mainly expressed in the cytoplasm of colon cells, with higher protein levels in tumor tissues compared to normal tissues (Fig. 4 C). Next, we tested the mRNA expression of GCSH in 15 clinical CRC tissues and paired adjacent tissues, as well as blood samples from 30 CRC patients and 30 healthy controls using RT-qPCR method. The results indicated higher mRNA expression of GCSH in CRC tissues and blood samples compared to adjacent tissues and healthy controls (Fig. 4 D-E), consistent with findings from the CRC datasets. Finally, we examined GCSH expression in three CRC cell lines (HCT116, HT29, and LoVo) and one normal human colon mucosal epithelial cell line (NCM460) using RT-qPCR method. The results showed higher mRNA expression of GCSH in CRC cells compared to NCM460, with the highest expression observed in HCT116 cells (Fig. 4 F). Exploring GCSH expression in CRC at scRNA-seq level Recently, scRNA-seq has emerged as a key method for cancer analysis [23]. Therefore, we utilized an scRNA-seq dataset to analyze GCSH expression in CRC. Figure 5 A summarizes the 12 cell types between tumor and normal tissues in the scRNA-seq dataset based on cell annotation. We compared GCSH expression between tumor and normal tissues in each cell type and found that GCSH was predominantly expressed in classical monocytes, HSC/MPP cells, plasma B cells, and plasmacytoid dendritic cells (Fig. 5 B). When comparing GCSH expression between tumor and normal tissues in these four cell types, we found higher GCSH expression in tumor tissues compared to normal tissues in classical monocytes and plasma B cells, but lower expression in HSC/MPP cells and plasmacytoid dendritic cells (Fig. 5 C). These results suggest that GCSH plays an important role in immune cells within CRC tissues. Function analysis of GCSH in CRC To explore the function of GCSH in CRC, we divided CRC patients into high- and low-expression groups using the median value of GCSH in the TCGA-COAD dataset (Fig. 6 A), and compared the clinical parameters between the two groups. We found that no significant difference between high- and low-expression of GCSH regarding the T sage, N stage and tumor stage except patients’ age, histological type and M stage (Table 1 ). Then we screened the DEGs between high- and low- expression, and the top 20 up-regulated and top 20 down-regulated genes are listed in Fig. 6 B. Gene Ontology (GO) analysis was performed for these genes, revealing that the up-regulated genes were mainly enriched in tRNA modification, RNA processing, mitochondrial transport, and folic acid metabolic processes, while the down-regulated genes were mainly enriched in regulation of ruffle assembly, heme metabolic process, and axon regeneration (Fig. 6 C-D). Next, we conducted KEGG pathway analysis for these DEGs using GSEA method, showing that these DEGs were mainly enriched in ATP synthesis coupled electron transport, mitochondrial ATP synthesis coupled electron transport, and mitochondrial respiratory chain complex assembly (Fig. 6 E). Finally, using the "CellMiner" package, we identified a significant positive correlation between GCSH and the IC50 value of 8-Chloro-adenosine (Fig. 6 F), indicating that cells with high GCSH expression were sensitive to 8-Chloro-adenosine treatment (Fig. 6 G). These findings suggest that CRC patients with high GCSH expression might benefit from this treatment. Table 1 Comparison of clinical parameters between high- and low-expression of GCSH in TCGA-COAD dataset Parameters High expression (N = 291) Low expression (N = 306) P value Age (years) 66.0 (56.0 to 74.0) 69.0 (61.0 to 78.0) 0.001 Gender 0.628 Female 134 (46%) 148 (48.4%) Male 157 (54%) 158 (51.6%) Location 0.587 Colon 215 (73.9%) 233 (76.1%) Rectal 76 (26.1%) 73 (23.9%) Histological type 0.027 Adenocarcinoma 263 (90.4%) 257 (84%) Mucinous 28 (9.6%) 49 (16%) T stage 0.621 T1 8 (2.7%) 11 (3.6%) T2 44 (15.1%) 57 (18.6%) T3 205 (70.4%) 204 (66.7%) T4 34 (11.7%) 34 (11.1%) N stage 0.902 N0 164 (56.4%) 178 (58.2%) N1 73 (25.1%) 73 (23.9%) N2 54 (18.6%) 55 (18%) M stage < 0.001 M0 209 (71.8%) 246 (80.4%) M1 40 (13.7%) 47 (15.4%) MX 42 (14.4%) 13 (4.2%) Tumor stage 0.587 I 47 (16.2%) 57 (18.6%) II 111 (38.1%) 117 (38.2%) III 93 (32%) 84 (27.5%) IV 40 (13.7%) 48 (15.7%) Discussion Copper, an indispensable micronutrient, is integral to various biological mechanisms, encompassing cellular respiration, antioxidant defenses, and connective tissue synthesis [24]. Disruptions in copper homeostasis are increasingly recognized as pivotal in disease etiology, particularly in oncological contexts. Alterations in copper metabolism, driven by gene dysregulation or mutation, can exacerbate malignancies through mechanisms such as enhanced oxidative stress and altered enzymatic activities, thereby promoting tumor proliferation, neovascularization, and metastatic spread [25]. This underscores the therapeutic potential of targeting copper homeostasis and associated pathways in cancer management. GCSH, a component of the glycine cleavage system, is situated on chromosome 16q23.2 and spans approximately 13.5 kb across 5 exons. Studies have linked GCSH to adverse outcomes in cholangiocarcinoma (CCA), where its inhibition curbs the malignancy of CCA cell lines in vitro [26]. Elevated GCSH expression has also been tied to poor prognosis in endometrial cancer, correlating with immune suppression and reduced efficacy of immunotherapy and chemotherapy [27]. Further, GCSH's involvement in nonketotic hyperglycinemia [28] and breast cancer cells viability [29] has been documented, highlighting its multifaceted role in disease. Our investigation represents the first comprehensive exploration of the role of GCSH across various cancers. Intriguingly, expression of GCSH varies widely—upregulated in some malignancies and downregulated in others, suggesting a dualistic function in distinct tumor microenvironments. Notably, gastrointestinal tumors exhibit particularly high GCSH levels. Using external cohorts, clinical samples, and CRC cell lines, we corroborated GCSH elevated expression in CRC, aligning with TCGA data. Despite preliminary evidence linking GCSH expression to prognosis in several cancers, external cohort validation was inconclusive, necessitating further substantiation with larger datasets. TME, comprising tumor, immune, and stromal components, has emerged as a critical determinant of tumor progression and immunotherapeutic response [30]. The role of GCSH in TME has been reported in pancreatic adenocarcinoma [31], cholangiocarcinomas [26], and endometrial cancer [27]. Our findings reveal GCSH was correlated with specific immune cell populations, tumor immune scores, and immune checkpoint genes across a broad spectrum of cancers, positioning it as a promising immunotherapeutic target. scRNA-seq analysis confirmed GCSH is predominant expresses in immune cells, reinforcing its TME relevance. Functional analyses elucidated GCSH biological functions and pathway involvement in CRC, culminating in the identification of 8-Chloro-adenosine as a potential therapeutic agent for CRC patients with high GCSH expression. Collectively, our study elucidates the effect of GCSH underpinnings in CRC development and treatment. Nevertheless, several limitations warrant acknowledgment. The differential expression of GCSH across cancers necessitates broader validation beyond CRC. The precise biological functions of GCSH in cancer, including its roles in pathogenesis, angiogenesis, metastasis, and treatment response, require further elucidation. Moreover, the mechanistic details of GCSH's involvement in cuproptosis within CRC demand experimental verification in vitro and in vivo . Conclusions The present our demonstrates the nuanced role of GCSH in cancer biology, specifically its associations with immune dynamics and checkpoint genes. We delineate GCSH functional landscape in CRC, along with its drug sensitivity implications. These findings enrich our understanding of GCSH's contribution to carcinogenesis and pave the way for novel therapeutic strategies targeting cancer through copper metabolism modulation. Declarations Ethics approval and consent to participate This study was approval by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Consent for publication Not applicable Competing interests The authors declare that they have no conflict of interest. Funding This study was partially supported by research funding from the National Natural Science Foundation of China (No. 82060104). Guangxi Natural Science Foundation(No. 2023GXNSFDA026015). Author Contribution Study concept and design: GXW, LRE and DZ; Collection and assembly of data: GXW and LSQ; Performed the experiment: GXW, LRE and LJ; Data analysis and interpretation: GXW, LRE and DZ; Manuscript writing and review: All authors. All authors have read and approved the manuscript in its current state. Data Availability The data used to support the findings of this study are available from the corresponding author upon request. 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Adamus A, Muller P, Nissen B, Kasten A, Timm S, Bauwe H, Seitz G, Engel N: GCSH antisense regulation determines breast cancer cells' viability . Sci Rep 2018, 8 (1):15399. Sadeghi Rad H, Monkman J, Warkiani ME, Ladwa R, O'Byrne K, Rezaei N, Kulasinghe A: Understanding the tumor microenvironment for effective immunotherapy . Med Res Rev 2021, 41 (3):1474–1498. Yan X, Zheng W, Xu FS, Chang HL, Zhang Y, Zhang ZY, Zhang YH: Identification and validation of a novel cuproptosis signature for stratifying different prognostic, immune, metabolic, and therapeutic landscapes in pancreatic adenocarcinoma . Eur Rev Med Pharmacol Sci 2024, 28 (5):2024–2050. Additional Declarations No competing interests reported. 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-5382756","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":390927765,"identity":"09326a75-69a8-4388-91d4-2b4a18b4b31c","order_by":0,"name":"Xian-wen Guo","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xian-wen","middleName":"","lastName":"Guo","suffix":""},{"id":390927767,"identity":"a1b26392-c007-4a91-b9d2-b62170d9fc9f","order_by":1,"name":"Rong-e Lei","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rong-e","middleName":"","lastName":"Lei","suffix":""},{"id":390927769,"identity":"a97017d2-2943-46e8-8ac2-ed92850e3c3d","order_by":2,"name":"Jiao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNvb2A4f/GNjI8fM3H3yQUFFDWAsfz5nEAzwFacaSM44lGzw4c4ywFjmJBOMDPB8OJ244kKMm+bCFmQiHSSQkHJAwAGk5w1aR2MDGwN/enYBfC8/DAwcMDNKNZx7uPXYjcYcMg8SZsxvwa2EH2pJgYC3bd+Bc2o3EM2wMBhK5BLQwJBgArWFmbDiQY1aQ2MZMhBaOBIODDQbOihOAWhiI08JzJuEwgwEkkCUSzhzjIegX+fb2w58Z/kCi8uOPiho5/vZe/FowAA9pykfBKBgFo2AUYAUAmTZSRKuj30EAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Li","suffix":""},{"id":390927770,"identity":"d52a774f-78eb-4c3b-ade3-dc8c46e4a733","order_by":3,"name":"Liqi Shen","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liqi","middleName":"","lastName":"Shen","suffix":""},{"id":390927771,"identity":"c64fe0ed-bc8e-425b-9963-a7c215437c14","order_by":4,"name":"Zhen Ding","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-11-03 15:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5382756/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5382756/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71618600,"identity":"41d97eb6-cc09-4c22-8436-a834153e5841","added_by":"auto","created_at":"2024-12-17 07:42:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of GCSH between normal and tumor tissues in 22 types of cancers. \u003c/strong\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/0384bbbb73ab0c7f6ac0248d.png"},{"id":71616611,"identity":"65187ee9-2231-499e-9d7e-eacccb80cdef","added_by":"auto","created_at":"2024-12-17 07:34:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of GCSH with TME and immune checkpoint gens in pan‑cancer. \u003c/strong\u003e(A) Correlation of GCSH expression with 22 immune cells in pan‑cancer; (B) Correlation of GCSH expression with tumor immune scores in pan‑cancer; (C) Correlation of GCSH expression with eight immune checkpoint genes in pan‑cancer. *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/ecb13a18f9d8405c64d3857e.png"},{"id":71616610,"identity":"632a789d-d1d6-4b4c-8f62-5ac4a3b557c7","added_by":"auto","created_at":"2024-12-17 07:34:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognosis value of GCSH with patient with cancer in pan‑cancer. \u003c/strong\u003e(A) Cox regression forest plot for GCSH in 33 types of cancer; Survival analysis for high- and low- expression of GCSH in (B) ACC dataset; (C) HCC dataset; (D) STAD dataset.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/6e4269bd06507cd35f560df3.png"},{"id":71616609,"identity":"b5c140c4-b83d-4a9b-a7ff-3c51188f7332","added_by":"auto","created_at":"2024-12-17 07:34:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of GCSH expression in CRC tissues and cells. \u003c/strong\u003eComparison of GCSH expression between tumor and normal tissues in (A) GSE106582 dataset; (B) GSE146889dataset; (C) Clinical tissue samples; (D) Clinical blood samples; (E) Reprehensive IHC image of GCSH in colon adenocarcinoma and normal tissue from HPA database; (E) Comparison of GCSH expression in normal colon cell (NCM460) and three CRC cells (HCT116, HT29 and LoVo).\u003cstrong\u003e \u003c/strong\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/85990b569721d76db2a223a7.png"},{"id":71616613,"identity":"021ffcc4-9a05-4425-b91d-5ed26dbeef48","added_by":"auto","created_at":"2024-12-17 07:34:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploring GCSH expression in CRC at scRNA-seq levels.\u003c/strong\u003e (A) The distribution of each cell type visualized by tSNE map; (B) Expression of GCSH in different cells; (C) Comparison of GCSH expression between tumor and normal tissues in Classical Monocytes, HSC/MPP cells, Plasma B cells, and Plasmacytoid Dendritic cells. *p\u0026lt;0.05, **p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/1be4ae7dd9921bcc4c72ed5e.png"},{"id":71616614,"identity":"2bf71e08-7c5a-4256-8284-aae4932ba477","added_by":"auto","created_at":"2024-12-17 07:34:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunction analysis of GCSH in CRC. \u003c/strong\u003e(A) Patients of TCGA-COAD dataset was divided into high- and low- expression group based on the median value of GCSH; (B) Heatmap of the top 20 up-regulated and 20 down-regulated genes between high- and low- expression group; (C) Biological process analysis for the up-regulated genes; (D) Biological process analysis for the down -regulated genes; (E) GSEA-KEGG pathway analysis for the differentially expressed genes between high- and low- expression group; (F) Correlation of GCSH expression with IC50 of 8-Chloro-adenosine; (G) Comparison of IC50 of 8-Chloro-adenosine between high and low GCSH expression. *p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/0dc7fbf67a73db3379c2c6c7.png"},{"id":86630132,"identity":"9ccb8c1e-e633-428a-abad-64d32a6f8bb7","added_by":"auto","created_at":"2025-07-14 06:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3195142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5382756/v1/623d7a8c-8496-4be5-b485-bc43c0b07e1d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCopper is an essential trace element that acts as a cofactor for over thirty enzymes involved in numerous physiological processes [1]. Recent research indicates that cells strictly regulate copper concentration, as elevated levels can be cytotoxic and even death [2]. Dysregulated copper homeostasis has been implicated in tumor development and metastasis [3]. Cuproptosis, a novel form of programmed cell death driven by copper toxicity, disrupts mitochondrial function, leading to cell death [4]. Altered copper metabolism is a common feature in cancer cells, making copper a potential target for cancer therapies [5]. Elevated copper levels aid cancer cell proliferation, angiogenesis, and metastasis [6].\u003c/p\u003e \u003cp\u003eCuproptosis-related genes significantly influence cancer progression by modulating copper levels and mitochondrial function [7]. To date, several such genes have been identified, including FDX1, SLC31A1, LIPT1, LIAS, DLD, DBT, GCSH, DLST, DLAT, PDHA1, PDHB, and ATP7A [8]. While some of these genes have been implicated in various cancers, others remain underexplored. For instance, SLC31A1 is a predictive biomarker in gynecological cancers [9]. DLD serves as a marker for early diagnosis and immunotherapy in osteomyelitis [10], and FDX1 is a potential diagnostic biomarker and therapeutic target for ovarian aging [11]. However, the roles of many cuproptosis-related genes in cancers remain to be fully elucidated.\u003c/p\u003e \u003cp\u003eGCSH (Glycine Cleavage System Protein H) is a critical cuproptosis-related gene [5]. It plays a vital role in the glycine cleavage system [12]. Defects in GCSH are linked to nonketotic hyperglycinemia, lipoate deficiency [13] and rheumatoid arthritis [14]. Elevated levels of GCSH have been observed in prostate adenocarcinoma [15], gliomas [16], and sarcoma [17]. However, its role in many other cancers remains under investigation. This study undertakes a comprehensive analysis of GCSH using multi-omics data in pan-cancer contexts and clinical samples, aiming to provide key insights into the effect of GCSH in tumorigenesis.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of pan-cancer datasets\u003c/h2\u003e \u003cp\u003eThe multi-omics data of pan-cancer cohorts and corresponding clinical data were sourced from the TCGAplot R package [18], which aggregates data from The Cancer Genome Atlas (TCGA) encompassing 33 types of cancers. The data were normalized to transcripts per million (log2(TPM\u0026thinsp;+\u0026thinsp;1)) values. Additionally, two Gene Expression Omnibus (GEO) datasets comprising 223 samples of primary colorectal cancer (CRC) and normal tissues\u0026mdash;GSE106582 (n\u0026thinsp;=\u0026thinsp;194) and GSE146889 (n\u0026thinsp;=\u0026thinsp;176)\u0026mdash;were retrieved for analysis. To evaluate survival outcomes, three GEO datasets with follow-up data were utilized, including GSE12417 (adrenocortical carcinoma, ACC, n\u0026thinsp;=\u0026thinsp;163), GSE76427 (hepatocellular carcinoma, HCC, n\u0026thinsp;=\u0026thinsp;167), and GSE84437 (stomach adenocarcinoma, STAD, n\u0026thinsp;=\u0026thinsp;433). All gene datasets underwent preprocessing, including background adjustment via the Robust Multi-array Average (RMA) algorithm. Immunocytochemistry images of GCSH in CRC were obtained from (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTumor microenvironment and immune checkpoint genes analysis\u003c/h3\u003e\n\u003cp\u003eThe CIBERSORT algorithm [19] was employed to quantify the proportion of immune infiltrating cells within the tumor microenvironment (TME) across different cancers, generating profiles of 22 human immune cell types based on gene expression data. Tumor immune scores, encompassing immune score, stromal score, and ESTIMATE score, were computed using the ESTIMATE algorithm. For immune checkpoint analysis, eight critical immune checkpoint genes were assessed: CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT [20].\u003c/p\u003e\n\u003ch3\u003eScRNA-seq analysis for CRC dataset\u003c/h3\u003e\n\u003cp\u003eThe scRNA-seq dataset of GSE132465 and annotation data were downloaded from GEO database, which included data of 23 primary CRC and 10 matched normal mucosa samples. The Seurat 4.2.0 R package was carried out to the processing of the scRNA-seq data according to the standard workflow. Initially, raw data is imported and converted into a Seurat object. Quality control is performed to filter out low-quality cells and genes. The data is then normalized and scaled. Highly variable genes are identified, followed by principal component analysis (PCA) to reduce dimensionality. Clustering is conducted based on significant principal components, and cluster biomarkers are identified. Finally, data visualization method of t-SNE is applied to visualize the clusters, and further downstream analyses can be performed. The markers used for cell identity were obtained from the ScType package [21].\u003c/p\u003e\n\u003ch3\u003eClinical CRC tissue and blood samples collection\u003c/h3\u003e\n\u003cp\u003eFrom January 2021 to February 2022, fifteen colorectal cancer (CRC) tissues and paired adjacent non-tumorous tissues were collected at our hospital. The inclusion criteria ensured that CRC patients had not received any treatments prior to surgery and were free from immune diseases, inflammatory diseases, or severe major organ dysfunction. Additionally, blood samples were obtained from 30 CRC patients and 30 healthy controls during the same period. A 10 mL peripheral venous blood sample was collected in an EDTA anticoagulant tube for peripheral blood mononuclear cell (PBMC) separation. The PBMCs were subsequently stored at -80\u0026deg;C until further processing. This study was approved by the hospital\u0026rsquo;s ethics committee, and written informed consent was obtained from each patient. The clinical features of the CRC samples are detailed in Table S1-S2.\u003c/p\u003e\n\u003ch3\u003eCRC cell lines culture\u003c/h3\u003e\n\u003cp\u003eThree CRC cell lines (HCT116, LoVo, and HT-29) and one human colon mucosal epithelial cell line (NCM460) were procured from the Chinese Academy of Sciences (Shanghai, China). HCT116, LoVo, and HT-29 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) with GlutaMAX (Gibco), supplemented with 10% fetal bovine serum (FBS) and 1% streptomycin/penicillin. NCM460 cells were cultured in phenol red\u0026ndash;free RPMI 1640 (Gibco), supplemented with 10% FBS and 1% streptomycin/penicillin.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and real‑time quantitative polymerase chain reaction (RT‑qPCR)\u003c/h2\u003e \u003cp\u003eUsing TRIzol reagent, total RNA was extracted and quantified using a NanoDrop spectrophotometer. Using a cDNA reverse transcription reagent, total RNA was reverse-transcribed. In a 7500 real-time PCR system with SYBR green, PCR amplification was performed at 95\u0026deg;C for 30 s, followed by 50\u003c/p\u003e \u003cp\u003ecycles of 95\u0026deg;C for 10 s and 60\u0026deg;C for 34 s. All primers were synthesized by company Jinweizhi (Tianjin, China). GAPDH primers Forward: 5\u0026prime;-TCA GCA ATG CCT CCT GCA C-3\u0026prime;, Reverse: 5\u0026prime;-TCT, GGG, TGG, CAG, TGA, TGG, C. SLC31A1 primers: 5\u0026prime;-3\u0026prime;Forward: GGG GAT GAG CTATATGGACTCC; Reverse: TCACCAA ACC GGAAAACAGTAG. The expression of GAPDH mRNA was used to normalize the expression of the target gene in each sample. Analyzing relative mRNA expression levels using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene functional enrichment analysis for GCSH in CRC\u003c/h3\u003e\n\u003cp\u003eTo identify differentially expressed genes (DEGs) associated with high and low expression of GCSH, we utilized the R \u0026ldquo;limma\u0026rdquo; package. The biological processes (BP) enrichment and KEGG pathway enrichment analyses for these DEGs in the TCGA-COAD dataset were performed using the TCGAplot R package. These analyses aimed to elucidate the functional roles of GCSH in CRC.\u003c/p\u003e\n\u003ch3\u003eDrug sensitivity analysis\u003c/h3\u003e\n\u003cp\u003eTo explore potential small-molecule drugs targeting GCSH, RNA expression data and drug sensitivity data for the NCI-60 cell lines were obtained from the \u0026ldquo;CellMiner\u0026rdquo; package [22]. We reanalyzed the correlation between GCSH expression and the efficacy of small-molecule drugs. Additionally, the sensitivity of these drugs was compared between high- and low-expression groups of GCSH.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were carried out using R software (version 4.3.0). Clinical features with a normal distribution were compared using the 2-tailed Student\u0026rsquo;s t-test, while those with an abnormal distribution were analyzed using the Wilcoxon rank-sum test. Differences in survival between the two risk groups were assessed using the Kaplan-Meier survival curve (KM) and log-rank test. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of differential expression of GCSH in pan-cancer\u003c/h2\u003e \u003cp\u003eWe compared the expression of GCSH between tumor and normal tissues across 33 types of cancers using the TCGA dataset. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, GCSH expression was significantly higher in tumor tissues compared to normal tissues in colon adenocarcinoma (COAD), bladder urothelial carcinoma (BLCA), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), kidney renal papillary cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), and rectum adenocarcinoma (READ) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, GCSH expression was lower in tumor tissues than in normal tissues in breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), sarcoma (SARC), and thyroid carcinoma (THCA) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in the remaining malignancies. These results suggest that GCSH expression is cancer type-dependent, with high expression predominantly in gastrointestinal tumors, particularly CRC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of GCSH with TME and immune checkpoint genes in pan‑cancer\u003c/h2\u003e \u003cp\u003eWe investigated the correlation of GCSH with the tumor microenvironment (TME) in pan-cancer using the TCGA dataset. The CIBERSORT and ESTIMATE algorithms were employed to determine the 22 types of immune cells and tumor immune scores, respectively. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, GCSH was significantly positively correlated with memory B cells, activated dendritic cells, resting NK cells, naive CD4\u0026thinsp;+\u0026thinsp;T cells, and follicular helper T cells, while negatively correlated with regulatory T cells (Tregs) across all 33 cancer types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlations of GCSH with other immune cells varied across the cancer types. Regarding the tumor immune scores, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB indicates that GCSH was significantly negatively correlated with stromal score, immune score, and ESTIMATE score across the 33 types of cancers. For the correlation of GCSH with eight immune checkpoint genes, we found that, except for uveal melanoma (UVM), GCSH was significantly negatively correlated with almost all immune checkpoint gene markers in the remaining 32 types of cancers. Interestingly, GCSH was positively correlated with SIGLEC15 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), pancreatic adenocarcinoma (PAAD), and thymoma (THYM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of GCSH in cancer patients across pan-cancer\u003c/h2\u003e \u003cp\u003eUsing the TCGA dataset, we examined the prognostic value of GCSH in cancer patients across pan-cancer using Cox regression analysis. As listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, a significant prognostic value of GCSH was observed only in adrenocortical carcinoma (ACC), liver hepatocellular carcinoma (LIHC), and stomach adenocarcinoma (STAD) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the remaining cancer types showed little prognostic value (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We further validated the association of GCSH with patient survival in ACC (GSE12417, n\u0026thinsp;=\u0026thinsp;163), LIHC (GSE76427, n\u0026thinsp;=\u0026thinsp;167), and STAD (GSE84437, n\u0026thinsp;=\u0026thinsp;433) using external cohorts. Using the median value of GCSH as the cut-off, the Kaplan-Meier plots showed no significant difference in survival time between high and low GCSH expression in patients with ACC, HCC, and STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D), suggesting that the prognostic value of GCSH in these patients requires further validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eValidation of GCSH expression in CRC tissues, blood and cells\u003c/h2\u003e \u003cp\u003eGiven that GCSH expression was significantly higher in gastrointestinal tumors, especially CRC, we selected CRC to validate GCSH expression. First, we analyzed GCSH expression in two large CRC datasets (GSE106582, n\u0026thinsp;=\u0026thinsp;194; GSE146889, n\u0026thinsp;=\u0026thinsp;176), and the results showed significantly increased GCSH expression in CRC tissues compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). The protein localization of GCSH was determined using the IHC method in the HPA database, revealing that GCSH was mainly expressed in the cytoplasm of colon cells, with higher protein levels in tumor tissues compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Next, we tested the mRNA expression of GCSH in 15 clinical CRC tissues and paired adjacent tissues, as well as blood samples from 30 CRC patients and 30 healthy controls using RT-qPCR method. The results indicated higher mRNA expression of GCSH in CRC tissues and blood samples compared to adjacent tissues and healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E), consistent with findings from the CRC datasets. Finally, we examined GCSH expression in three CRC cell lines (HCT116, HT29, and LoVo) and one normal human colon mucosal epithelial cell line (NCM460) using RT-qPCR method. The results showed higher mRNA expression of GCSH in CRC cells compared to NCM460, with the highest expression observed in HCT116 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExploring GCSH expression in CRC at scRNA-seq level\u003c/h2\u003e \u003cp\u003eRecently, scRNA-seq has emerged as a key method for cancer analysis [23]. Therefore, we utilized an scRNA-seq dataset to analyze GCSH expression in CRC. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA summarizes the 12 cell types between tumor and normal tissues in the scRNA-seq dataset based on cell annotation. We compared GCSH expression between tumor and normal tissues in each cell type and found that GCSH was predominantly expressed in classical monocytes, HSC/MPP cells, plasma B cells, and plasmacytoid dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). When comparing GCSH expression between tumor and normal tissues in these four cell types, we found higher GCSH expression in tumor tissues compared to normal tissues in classical monocytes and plasma B cells, but lower expression in HSC/MPP cells and plasmacytoid dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These results suggest that GCSH plays an important role in immune cells within CRC tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunction analysis of GCSH in CRC\u003c/h2\u003e \u003cp\u003eTo explore the function of GCSH in CRC, we divided CRC patients into high- and low-expression groups using the median value of GCSH in the TCGA-COAD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and compared the clinical parameters between the two groups. We found that no significant difference between high- and low-expression of GCSH regarding the T sage, N stage and tumor stage except patients\u0026rsquo; age, histological type and M stage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Then we screened the DEGs between high- and low- expression, and the top 20 up-regulated and top 20 down-regulated genes are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB. Gene Ontology (GO) analysis was performed for these genes, revealing that the up-regulated genes were mainly enriched in tRNA modification, RNA processing, mitochondrial transport, and folic acid metabolic processes, while the down-regulated genes were mainly enriched in regulation of ruffle assembly, heme metabolic process, and axon regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). Next, we conducted KEGG pathway analysis for these DEGs using GSEA method, showing that these DEGs were mainly enriched in ATP synthesis coupled electron transport, mitochondrial ATP synthesis coupled electron transport, and mitochondrial respiratory chain complex assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Finally, using the \"CellMiner\" package, we identified a significant positive correlation between GCSH and the IC50 value of 8-Chloro-adenosine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), indicating that cells with high GCSH expression were sensitive to 8-Chloro-adenosine treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). These findings suggest that CRC patients with high GCSH expression might benefit from this treatment.\u003c/p\u003e \u003cp\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\u003eComparison of clinical parameters between high- and low-expression of GCSH in TCGA-COAD dataset\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\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh expression (N\u0026thinsp;=\u0026thinsp;291)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow expression (N\u0026thinsp;=\u0026thinsp;306)\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0 (56.0 to 74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0 (61.0 to 78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\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=\"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\u003e0.628\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\u003e134 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (48.4%)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (51.6%)\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\u003eLocation\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\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (76.1%)\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\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (23.9%)\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\u003eHistological type\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\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257 (84%)\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\u003eMucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (16%)\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 stage\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\u003e0.621\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\u003e8 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.6%)\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (18.6%)\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\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (66.7%)\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\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (11.1%)\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\u003eN stage\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\u003e0.902\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\u003e164 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (58.2%)\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\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (23.9%)\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\u003e54 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (18%)\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 stage\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\u003e\u0026lt;\u0026thinsp;0.001\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\u003e209 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246 (80.4%)\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\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (15.4%)\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\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.2%)\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\u003eTumor stage\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\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (18.6%)\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\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (38.2%)\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\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (27.5%)\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\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (15.7%)\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 \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCopper, an indispensable micronutrient, is integral to various biological mechanisms, encompassing cellular respiration, antioxidant defenses, and connective tissue synthesis [24]. Disruptions in copper homeostasis are increasingly recognized as pivotal in disease etiology, particularly in oncological contexts. Alterations in copper metabolism, driven by gene dysregulation or mutation, can exacerbate malignancies through mechanisms such as enhanced oxidative stress and altered enzymatic activities, thereby promoting tumor proliferation, neovascularization, and metastatic spread [25]. This underscores the therapeutic potential of targeting copper homeostasis and associated pathways in cancer management.\u003c/p\u003e \u003cp\u003eGCSH, a component of the glycine cleavage system, is situated on chromosome 16q23.2 and spans approximately 13.5 kb across 5 exons. Studies have linked GCSH to adverse outcomes in cholangiocarcinoma (CCA), where its inhibition curbs the malignancy of CCA cell lines in vitro [26]. Elevated GCSH expression has also been tied to poor prognosis in endometrial cancer, correlating with immune suppression and reduced efficacy of immunotherapy and chemotherapy [27]. Further, GCSH's involvement in nonketotic hyperglycinemia [28] and breast cancer cells viability [29] has been documented, highlighting its multifaceted role in disease.\u003c/p\u003e \u003cp\u003eOur investigation represents the first comprehensive exploration of the role of GCSH across various cancers. Intriguingly, expression of GCSH varies widely\u0026mdash;upregulated in some malignancies and downregulated in others, suggesting a dualistic function in distinct tumor microenvironments. Notably, gastrointestinal tumors exhibit particularly high GCSH levels. Using external cohorts, clinical samples, and CRC cell lines, we corroborated GCSH elevated expression in CRC, aligning with TCGA data. Despite preliminary evidence linking GCSH expression to prognosis in several cancers, external cohort validation was inconclusive, necessitating further substantiation with larger datasets.\u003c/p\u003e \u003cp\u003eTME, comprising tumor, immune, and stromal components, has emerged as a critical determinant of tumor progression and immunotherapeutic response [30]. The role of GCSH in TME has been reported in pancreatic adenocarcinoma [31], cholangiocarcinomas [26], and endometrial cancer [27]. Our findings reveal GCSH was correlated with specific immune cell populations, tumor immune scores, and immune checkpoint genes across a broad spectrum of cancers, positioning it as a promising immunotherapeutic target. scRNA-seq analysis confirmed GCSH is predominant expresses in immune cells, reinforcing its TME relevance. Functional analyses elucidated GCSH biological functions and pathway involvement in CRC, culminating in the identification of 8-Chloro-adenosine as a potential therapeutic agent for CRC patients with high GCSH expression.\u003c/p\u003e \u003cp\u003eCollectively, our study elucidates the effect of GCSH underpinnings in CRC development and treatment. Nevertheless, several limitations warrant acknowledgment. The differential expression of GCSH across cancers necessitates broader validation beyond CRC. The precise biological functions of GCSH in cancer, including its roles in pathogenesis, angiogenesis, metastasis, and treatment response, require further elucidation. Moreover, the mechanistic details of GCSH's involvement in cuproptosis within CRC demand experimental verification \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present our demonstrates the nuanced role of GCSH in cancer biology, specifically its associations with immune dynamics and checkpoint genes. We delineate GCSH functional landscape in CRC, along with its drug sensitivity implications. These findings enrich our understanding of GCSH's contribution to carcinogenesis and pave the way for novel therapeutic strategies targeting cancer through copper metabolism modulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approval by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was partially supported by research funding from the National Natural Science Foundation of China (No. 82060104). Guangxi Natural Science Foundation(No. 2023GXNSFDA026015).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eStudy concept and design: GXW, LRE and DZ; Collection and assembly of data: GXW and LSQ; Performed the experiment: GXW, LRE and LJ; Data analysis and interpretation: GXW, LRE and DZ; Manuscript writing and review: All authors. All authors have read and approved the manuscript in its current state.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen L, Min J, Wang F: \u003cb\u003eCopper homeostasis and cuproptosis in health and disease\u003c/b\u003e. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e 2022, \u003cb\u003e7\u003c/b\u003e(1):378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGe EJ, Bush AI, Casini A, Cobine PA, Cross JR, DeNicola GM, Dou QP, Franz KJ, Gohil VM, Gupta S \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eConnecting copper and cancer: from transition metal signalling to metalloplasia\u003c/b\u003e. \u003cem\u003eNat Rev Cancer\u003c/em\u003e 2022, \u003cb\u003e22\u003c/b\u003e(2):102\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e 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Shukla A: \u003cb\u003eBiallelic start loss variant, c.1A\u0026thinsp;\u0026gt;\u0026thinsp;G in GCSH is associated with variant nonketotic hyperglycinemia\u003c/b\u003e. \u003cem\u003eClin Genet\u003c/em\u003e 2021, \u003cb\u003e100\u003c/b\u003e(2):201\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdamus A, Muller P, Nissen B, Kasten A, Timm S, Bauwe H, Seitz G, Engel N: \u003cb\u003eGCSH antisense regulation determines breast cancer cells' viability\u003c/b\u003e. \u003cem\u003eSci Rep\u003c/em\u003e 2018, \u003cb\u003e8\u003c/b\u003e(1):15399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadeghi Rad H, Monkman J, Warkiani ME, Ladwa R, O'Byrne K, Rezaei N, Kulasinghe A: \u003cb\u003eUnderstanding the tumor microenvironment for effective immunotherapy\u003c/b\u003e. \u003cem\u003eMed Res Rev\u003c/em\u003e 2021, \u003cb\u003e41\u003c/b\u003e(3):1474\u0026ndash;1498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan X, Zheng W, Xu FS, Chang HL, Zhang Y, Zhang ZY, Zhang YH: \u003cb\u003eIdentification and validation of a novel cuproptosis signature for stratifying different prognostic, immune, metabolic, and therapeutic landscapes in pancreatic adenocarcinoma\u003c/b\u003e. \u003cem\u003eEur Rev Med Pharmacol Sci\u003c/em\u003e 2024, \u003cb\u003e28\u003c/b\u003e(5):2024\u0026ndash;2050.\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":"GCSH, pan-cancer, colorectal cancer, immune analysis, function analysis","lastPublishedDoi":"10.21203/rs.3.rs-5382756/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5382756/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGCSH, a gene associated with cuproptosis, has been implicated in various cancers, although its role remains incompletely understood. This study aims to conduct a comprehensive analysis of GCSH across multiple cancer types to elucidate its role in tumorigenesis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGCSH expression was analyzed in 33 cancer types using data from TCGA database. Associations with the tumor microenvironment and prognostic value were evaluated. scRNA-seq data from colorectal cancer (CRC) was used to assess GCSH expression in different cell types. Clinical CRC tissues, blood samples, and cell lines were utilized for validation. Functional assays and drug sensitivity tests were performed to further elucidate the role of GCSH.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGCSH expression varied among different cancers, with notably higher levels in CRC. GCSH demonstrated significant correlations with 22 types of immune cells across the 33 cancers. Generally, GCSH showed a negative correlation with immune scores and immune checkpoint genes. Prognostic analysis revealed that GCSH was associated with outcomes in adrenocortical carcinoma, hepatocellular carcinoma, and stomach adenocarcinoma, although external cohort results did not consistently support these findings. Validation in clinical samples and cell lines confirmed elevated GCSH in CRC. scRNA-seq data indicated higher GCSH expression in both cancerous and immune cells within tumor tissues compared to normal tissues. Functional and pathway analyses in CRC identified key biological roles for GCSH, and a drug sensitivity to GCSH was identified.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGCSH exerts multifaceted roles in specific cancers and is significantly associated with immune cells and immune checkpoint genes. The study identifies the biological functions of GCSH in CRC and suggests potential drug sensitivities.\u003c/p\u003e","manuscriptTitle":"A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 07:34:00","doi":"10.21203/rs.3.rs-5382756/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":"b3223ee9-2e76-4304-8f31-c033c29af9a1","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41664175,"name":"Health sciences/Diseases"},{"id":41664176,"name":"Health sciences/Gastroenterology"},{"id":41664177,"name":"Health sciences/Health care"},{"id":41664178,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-07-14T06:09:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 07:34:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5382756","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5382756","identity":"rs-5382756","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}