{"paper_id":"0ee5297c-c09d-464b-b3f9-e640c70864b0","body_text":"PPT2 Promotes the Rectal Cancer via CD4+ Immune Cells | 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 PPT2 Promotes the Rectal Cancer via CD4+ Immune Cells Gengchen Lu, Yining Zhou, Rui Wang, Lingwei Song, Zhiwei Xu, Bin Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7128416/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 Recent studies have revealed the importance of protein palmitoylation in controlling immune responses and sustaining the balance of intestinal flora. Furthermore, this process is associated with a variety of tumor diseases. However, the mechanisms by which palmitoylation-related genes affect rectal cancer via immune or microecological pathways have not been systematically studied. Methods This study uses Mendelian randomization and mediation analysis to explore the causal interactions and potential mechanisms between palmitoylated genes and immune cell phenotypes, intestinal flora and rectal cancer. Based on two-sample MR analysis and mediation analysis, this study integrates genome-wide association study data of rectal cancer from FinnGen database, immune cell phenotype data from INTERVAL project and GWAS data of intestinal flora from MiBioGen. The study firstly evaluated the causal relationship between palmitoylation-related genes and rectal cancer risk, and then further explored the mediating mechanisms of immune cell characteristics and intestinal flora using mediation analysis. Results The results showed that high expression of the PPT2 gene significantly increased the risk of rectal cancer, and its pathogenic effect was mediated partly through the CD4+ T-cell phenotype. Mediation analyses revealed that the regulatory effect of PPT2 on CD127 high-expressing T-cell subsets (Mediated proportion= 20.66%) may promote immune escape. Alternatively, it may weaken the anti-tumor function of T cells at the functional level by downregulating the expression of FSC-A (Mediated proportion = 29.96%) and SSC-A (Mediated proportion = 24.86%). The results of the correlation analysis showed good robustness in multiple statistical models. Conclusion The present study reveals the regulatory role of palmitoylation modification in immune regulation, clarifies the potential mechanism of PPT2 in rectal carcinogenesis, and suggests that palmitoylation-related genes can be used as early warning biomarkers and potential research directions for intervention targets. Rectal Cancer Palmitoylation PPT2 Immune Cells Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Rectal malignant tumor is a common cancer of the digestive system, and is characterized by high incidence, high lethality, and high recurrence rate, posing a serious threat to human health. The pathogenesis of rectal malignant tumor is complex and is affected by multiple factors such as genetics, lifestyle, dietary structure and inflammatory state. The process of rectal cancer is closely related to the changes in the composition and diversity of the intestinal microbiota, and the intestinal microbiota can cause tumor occurrence and development by affecting the immune function and metabolic process of the host [1–3]. In addition, abnormal immune function is also a crucial factor leading to rectal cancer. Imbalance in the regulation of the immune system in chronic inflammatory states may lead to tumor occurrence and development. The interaction between intestinal microbiota and immune cells is crucial for tumor immunosurveillance, with its dysregulation potentially facilitating rectal carcinogenesis [4–6]. Protein post-translational modifications (PTMs) refer to covalent binding of small chemical molecular groups to amino acid side chains of proteins via enzymatic reactions after the protein-synthesis is finished, thus modifying protein structure and function [7]. Recent studies have increasingly revealed the crucial role of protein PTMs in regulating tumor occurrence and development. In tumor biology, PTMs are involved in tumor occurrence, development, invasion, and metastasis by affecting biological processes such as signal transduction, cell cycle, metabolic reprogramming and immune responses[8] Palmitoylation is a significant form of reversible lipid modification and widely exists in a variety of proteins within cellular environments. Palmitoylation regulates the subcellular localization, stability and functional state of proteins by binding palmitoyl groups to cysteine residues of proteins to form thioester bonds. This modification plays a crucial role in numerous biological processes, including signal transduction, the transport and localization of membrane proteins, and interactions between protein and other molecules[9, 10]. In the field of cancer research, it is found that palmitoylation is significantly associated with tumor occurrence and immunotherapy. Palmitoylation can affect immune evasion and drug tolerance mechanisms of tumor cells by means of T cell activation, cytokine signal transduction as well as autophagy and phagocytosis[11]. Besides, palmitoylation abnormality can affect immune cell function and intestinal microbial composition and thus can further affect immune surveillance and the development of tumors. [12, 13]. Palmitoylation is involved in tumor occurrence and development of rectal cancer by affecting cell signaling pathways and protein function [14]. Palmitoylation affects intestinal inflammation and barrier function by regulating the palmitoylation and phosphorylation of signal transduction and activator of transcription 3 (STAT3) [15]. These findings indicate that palmitoylation plays a crucial role in the occurrence and development of rectal cancer by regulating immune responses and intestinal barrier function. In summary, although the role of palmitoylation in immune signal transduction, inflammatory response and intestinal barrier regulation has been partially revealed, its specific mechanism in rectal cancer still requires further study. Mendelian randomization (MR) is an epidemiological research method based on genetic variation, which is widely used to assess causal relationships between traits and diseases with significant advantages [16]. Genome-Wide Association Study (GWAS) is a scientific approach that identifies genetic variants associated with diseases or traits by analyzing single nucleotide polymorphisms (SNP) associated with phenotypic traits. In addition, the integration of multi-omics data, encompassing gene expression and epigenetic modifications (e.g., DNA methylation), can facilitate the identification of expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL), and thereby enhance our understanding of the function of genetic variants [17, 18]. Compared with conventional observational studies, MR analysis can simulate the study design of a Randomized Controlled Trial (RCT), which is more reliable in inferring causality. Furthermore, MR analysis can effectively reduce the interference of confounding factors and avoid the bias caused by reverse causality [19]. eQTL which affects the genetic variants of gene expression level or SNP can regulate the expression of specific genes in the genome of an individual [20]. The study of eQTL can facilitate the acquisition of insights into the genetic factors that regulate gene expression, and thereby further reveal the mechanisms that link the function of genes to expression traits [21]. Considering the potential relationship between palmitoylation in the field of immune regulation and fields of gut microbial function and oncology, this study, using multiple statistical genetic approaches, aims to systematically investigate the mechanism of palmitoylation-related genes in regulating the intestinal flora, immune cellular composition and their combined effects on rectal cancer, expecting to provide new targets and strategies for disease treatment. 2 Materials and methods 2.1 Research design This study investigates the mechanistic role of palmitoylation genes in the development and progression of rectal cancer through a multi-stage analysis (Fig. 1 ). First, known palmitoylation genes were extracted through literature search and intersected with filtered eQTL data to identify palmitoylation genes associated with rectal cancer. Then, expression differential analysis was conducted using the GEO database to validate and determine the target genes. Mendelian randomization (MR) methods were used to explore the causal relationships between immune cells, gut microbiota, and rectal cancer. Finally, MR analysis results were associated with the target genes, and a mediation analysis was performed to quantify the effect proportion of the target genes in the immune cells and gut microbiota influencing the progression of rectal cancer, revealing the potential mediating role of palmitoylation genes. 2.2 Literature Search Strategy and Palmitoylation Gene Collation Relevant literature was searched through databases (e.g., PubMed) for palmitoylated genes published before the cut-off date. Searches were conducted using the following keywords: 'palmitoylation' and 'gene'. The inclusion criteria for the literature were as follows: (1) Strong evidence supporting the authenticity of the palmitoylated genes; (2) Information including the first author, year of publication, country or region, and type of study; (3) Authenticity of the article with an impact factor of 2 or higher. The exclusion criteria were: (1) Palmitoylated genes not clearly named; (2) Genes that could not be clearly categorized or were ambiguous; (3) Case reports or conference abstracts. 2.3 eQTL Dataset The eQTL data of all genes in this study were obtained from the eQTLGen database ( https://eqtlgen.org ). All data were cis-eQTL (cis-eQTL), derived from blood samples. The instrumental variables (SNPs) were subjected to screening according to the following parameters: p-value < 5e-8, clumping window :10000kb, R 2 < 0.1. This process resulted in the identification of 15,695 genes with cis-eQTL data. 2.4 Endings dataset Rectal malignancy data were obtained from the FinnGenc R12 database ( https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_R12_C3_RECTUM_EXALLC.gz ). The disease identification code was C3_RECTUM_EXALLC, which included a total of 4,844 rectal cancer cases and 378,749 controls. All participants were from the European population. 2.5 Two-sample Mendelian randomisation analysis MR uses genetic variation to represent risk factors, and the genetic instrumental variables (IVs) used in the analyses must satisfy three key assumptions (Fig. 1 )[22]: (1) The instrumental variables are significantly associated with the exposure factors. (2) The instrumental variables are independent of any confounders of the outcome association. (3) The instrumental variables affect the outcome only through their influence on the exposure factors and not by other means. The two-sample Mendelian randomisation analysis was conducted in this study by utilising the TwoSampleMR package. The cis-eQTL data corresponding to the palmitoylation gene was used as the exposure, and rectal cancer was used as the outcome. In order to reduce the bias caused by weak instrumental variables, instrumental variables with F values less than 10 were excluded from the analysis. In this study, the primary algorithm employed was the inverse variance weighted (IVW) method, with the objective of excluding exposures for which it was not possible to calculate IVW outcomes. The robustness of the results was assessed using the MR-Egger intercept test to detect multiplicity, Cochran's Q to detect heterogeneity, and sensitivity analysis using the leave-one-out method. 2.6 Expression difference analysis Rectal cancer microarray data were obtained from the Gene Expression Omnibus (GEO) database, dataset ID GSE75970, which contains a total of 32 diseased tissues and 96 normal tissues, for a total of 128 samples. Differential expression analysis was performed using the limma package, and genes were considered to be differentially expressed if they met the following criteria: disease tissue was higher or lower than normal tissue, and the p-value was less than 0.05. 2.7 Intermediary analysis We used two-step MR mediator analysis to explore whether palmitoylated genes play a role in promoting disease by regulating immune cells and gut flora. Immune cell data (data ID: ebi-a-GCST90001391- ebi-a-GCST90002121) and gut flora data were downloaded from the IEU OpenGWAS project ( https://gwas.mrcieu.ac.uk/ ). Instrumental variables (SNPs) were screened according to the following parameters: p-value < 5e-8, clumping window :10000kb, r 2 < 0.1, and finally 612 immune cell data and 473 intestinal flora data were obtained. First, calculate the effect size of palmitoylated gene eQTL to rectal cancer, labeled as BetaT; then calculate the effect size of palmitoylated gene eQTL to immune cells and intestinal flora, labeled as BetaA, and the effect size of immune cells and intestinal flora to rectal cancer, labeled as BetaB. Finally, calculate the mediator effect (BetaAB): BetaA*BetaB, the Mediated proportion (P betaAB ):(BetaAB/BetaT) *100%. Results in which the mediating effect was not in the same direction as the total effect were excluded, and results with a mediating effect proportion greater than 20% were selected for analysis. 2.8 Statistical methods All statistical analyses in this study were performed using the opensource free R software (version 4.4.2), and the study was conducted using R packages such as “Mendelian Randomization” and “MRPRESSO”, Mendelian Randomization”, ‘MRPRESSO’, ‘TwoSampleMR’ and other R packages were used for the analysis. PLINK software (version v1.90) was used to perform the local removal of chain imbalances. The threshold of statistical significance was set at p-value < 0.05. 3 Results 3.1 Palmitoylation gene-eQTL A retrospective analysis of the palmitoylation literature was conducted to identify 31 palmitoylation genes that have been confidently associated[13, 23, 24]. These genes were then filtered to identify 22 co-located genes by taking intersections with the 15,695 eQTL data obtained after local elimination of linkage disequilibrium (Fig. 2 ). 3.2 Palmitoylation eQTL gene- Rectal cancer The identified 22 palmitoylated eQTL genes with rectal cancer were subjected to batch Mendelian randomization analysis, and a total of three loci were obtained by extracting the P < 0.05 results based on the IVW results and visualizing the expression in forest plots (Fig. 3 ). 3.3 Differential Expression Analysis The above genes were validated by differential expression analysis and visualized for expression (Fig. 4 ). The discrepancy between the findings was found to be statistically significant, with the odds ratio (OR) value meeting the 95% confidence interval (CI) requirement. Furthermore, all the OR values obtained with eQTL were found to be consistent with each other. Consequently, PPT2, ZDHHC14 and ZDHHC20 were identified as the final target genes (Table 1 ), with PPT2(OR = 1.2244, 95% CI = 1.0544–1.4217, P = 0.0079) and ZDHHC20(OR = 1.1322, 95% CI = 1.0165–1.2611, P = 0.0239) being classified as risk factors for rectal cancer and ZDHHC14(OR = 0.9238, 95% CI = 0.8726–0.9807, P = 0.0094) as a protective factor. Table 1 Target palmitoylated genes ID Nsnp Method B SE OR (95%CL) Pvalue PPT2 5 IVW 0.2024 0.0762 1.2244(1.0544 ~ 1.4217) 0.0079 ZDHHC14 40 IVW -0.0793 0.0305 0.9238(0.8702 ~ 0.9807) 0.0094 ZDHHC20 19 IVW 0.1242 0.0550 1.1322(1.0165 ~ 1.2611) 0.0239 3.4 Rectal cancer-associated immune cells and intestinal flora Screening of immune cells and intestinal flora associated with rectal cancer: a batch two-sample Mendelian randomization study with immune cells and intestinal flora as exposure and rectal cancer as outcome, respectively, yielded a total of 27 immune cells (Fig. 5 ) and 29 intestinal microbiota (Fig. 6) associated with rectal cancer. Figure 6 Forest plot of intestinal microbiota associated with rectal cancer 3.5 Target genes-associated immune cells and intestinal flora Conduct MR analysis of the target genes with the above-mentioned immune cells and intestinal microbiota, and perform mediation analysis by integrating all the results (Supplementary material). Based on the results of screening the data in the table for a mediating effect proportion of not less than 20% (P betaAB ≥ 20%), it can be preliminarily concluded that PPT2 promotes rectal cancer through CD127 on CD45RA + CD4+ (P betaAB = 20.66%) FSC-A on CD4+ (P betaAB = 29.96%) and SSC-A on CD4+ (P betaAB = 24.86%) mediated effects promote rectal carcinogenesis (Table 2 ). Table 2 Results of the downstream mediation analysis of PPT2 after screening Immune cell phenotype BetaT BetaA BetaB BetaAB SE Z P betaAB CD127 on CD45RA + CD4+ 0.2024 0.4599 0.0909 0.0418 0.0645 0.6482 20.66% FSC-A on CD4+ 0.2024 -0.3465 -0.1750 0.0606 0.0358 1.6916 29.96% SSC-A on CD4+ 0.2024 -0.3666 -0.1373 0.0503 0.0375 1.3414 24.86% BetaT: The total effect of PPT2 on rectal cancer; BetaA: The effect of PPT2 on immune cells; BetaB: The effect value of immune cells on rectal cancer; BetaAB: The mediating effect, that is, the introduced effect, which represents the effect of PPT2 on rectal cancer mediated by immune cells; SE is the standard error; Z is the statistic; P BetaAB is the proportion of the mediating effect. 4 Disscusion In this study, we employed two-sample Mendelian randomization to reveal the potential impact of palmitoylation-related genes on the development of rectal cancer. Further mediation analysis indicated that PPT2 may promote rectal cancer initiation by modulating T cell activation phenotypes. PPT2 (Palmitoyl-Protein Thioesterase 2) is a member of the depalmitoylase family, and its main function is to catalyze the hydrolysis of palmitoyl groups on the surface of proteins and to regulate the membrane localization, stability, and signaling behavior of proteins. Depalmitoylases are involved in regulating the lipid composition and dynamics of cell membranes, which is essential for maintaining the integrity and function of cell membranes [25, 26]. In addition, depalmitoylase may affect cell metabolism and growth by regulating membrane anchoring of proteins and signaling[27]. In recent years more and more studies have begun to focus on the potential role of PPT2 in tumor immunomodulation, especially in the regulation of T cell function. It has been shown that PPT2 may influence the effect of immune response in the tumor microenvironment by regulating T cell function and survival, and this process may be closely related to its regulation of protein palmitoylation level [28, 29]. In the present study, PPT2 significantly mediated rectal cancer risk through CD4 + T cells. Among them, CD127 on CD45RA + CD4 + was a risk factor for rectal cancer, while FSC-A on CD4 + and SSC-A on CD4 + were protective factors for rectal cancer, thus PPT2 promotes rectal cancer through bidirectional regulation of different phenotypes of immune cells. In tumor immunity, the role of CD4 + T cells is not limited to the activation and proliferation of adjuvant CD8 + T cells, but also includes direct involvement in the recognition and clearance of tumor cells. However, when the function of CD4 + T cells is suppressed or transformed into regulatory T cells (Treg), they may inhibit anti-tumor immune responses and promote immune escape from tumors [30]. Studies have shown that tumor cells can induce a functional shift in CD4 + T cells through multiple mechanisms, such as inhibiting CD4 + T cell activity by upregulating the expression of immune checkpoint molecules PD-1 and PD-L1 [31]. CD127 (IL-7Rα) plays an important role in the tumor immune microenvironment, and its high expression is often associated with T cell over-activation, functional exhaustion, and immune tolerance, which plays an important role in tumor immunotherapy [32, 33]. It has been shown that CD127-overexpressing CD4 + T cell subpopulations can form an “immunopseudoquiescent” state in tumor tissues, which not only makes it difficult to clear tumor cells, but also may be “recruited” by tumor cells as an immune barrier. Therefore, PPT2 may affect the membrane localization or internalization of CD127 by regulating the level of palmitoylation of T cell membrane proteins, thus promoting the formation of an immune tolerance phenotype in this subpopulation. Comparatively, FSC-A and SSC-A indexes are usually used to reflect T cell volume and cell granule complexity, which are important parameters for measuring the activation and functional status of T cells [34]. It has been shown that activated T cells play an important role in the immune microenvironment of rectal cancer and are closely related to tumor progression and prognosis[35–37]. Both are rectal cancer protective factors, suggesting that activated T cells may play a positive role in tumor clearance, and PPT2, by down-regulating these two parameters, may have weakened the anti-tumor effect of T cells at the functional level. Taken together, PPT2 may bi-directionally regulate different phenotypes of T cells through depalmitoylation modification, forming a favorable immune microenvironment for rectal cancer. Notably, palmitoylation, as a dynamic and reversible lipid modification, possesses the ability to finely regulate cell signaling and membrane protein functions. It has been reported at several key nodes such as T cell receptor complexes, immune checkpoint proteins (e.g., PD-L1), and inflammatory signaling molecules (e.g., NOD2) [38, 39]. For example, inhibition of palmitoylation of PD-L1 enhances the immune response of T cells against tumors [40]. In addition, palmitoylation has been found to regulate the ability of intestinal epithelial cells to sense microbe-associated molecular patterns (MAMPs), a potential regulatory hub in host-microecology interactions [41, 42]. For example, this study also found that ZDHHC20 increases the risk of rectal cancer through UBA3282 sp002493835l (Mediated proportion = 15.66%) and Prevotella sp900317685 (Mediated proportion = 11.32%),moreover, ZDHHC14 was found to reduce the risk of rectal cancer through CAG-273 sp003534295 (Mediated proportion = 18.85%) (Supplementary Material).Therefore, palmitoylated genes may not only be regulatory factors in rectal carcinogenesis, but also central to the interaction between host genetics, immune status and microcosmic environment. The regulatory mechanism proposed in this study provides a new perspective for understanding the integration of genetic background and external environmental factors in tumorigenesis and lays a theoretical foundation for the development of preventive or therapeutic strategies targeting the palmitoylation pathway in the future. 5 Limitation Several limitations of the present study remain. First, the genetic and phenotypic data used in this study were mainly derived from European populations, especially represented by the Finnish population, which may have an ethnostructural bias that limits the applicability of the findings to other populations. Therefore, future validation in a wider range of ethnic and regional populations is necessary to improve the generalizability and extrapolation of the findings. Second, although this study initially revealed the potential mechanism of PPT2 in rectal carcinogenesis through two-sample Mendelian randomization and mediation analysis, it lacked direct validation of their regulatory effects and molecular mechanisms in ex vivo experiments (e.g., cellular function experiments or animal models), especially the identification of palmitoylated modification sites and their functional impacts still need to be explored in depth. In addition, the study failed to fully incorporate potential interactions between environmental factors (e.g., diet, inflammatory state, or intestinal barrier disruption) and palmitoylation regulation, which may play key roles in immune regulation and changes in the gut flora structure, and future studies may combine epigenetic and environmental exposure data for integrated analysis 6 Conclusion In this study, two-sample Mendelian randomization and mediation analysis were employed to systematically investigate the potential mechanisms by which palmitoylation-related genes contribute to the development of rectal cancer. The results demonstrated that high expression of the PPT2 gene significantly increased the risk of rectal cancer, with its pathogenic effect partially mediated by changes in the phenotype of CD4⁺ T-cell subpopulations. Specifically, PPT2 may promote tumor immune escape by modulating the functional state of CD127 high-expressing T-cell subsets. Additionally, PPT2 might impair the anti-tumor activity of T cells by downregulating activation-associated parameters such as FSC-A and SSC-A. Robustness analyses using multiple statistical models confirmed the reliability of these findings. This study is the first to elucidate, from a genetic causal perspective, the link between palmitoylation modification, immune regulation, and tumorigenesis. It provides new insights into the role of PPT2 in shaping the tumor immune microenvironment and offers a theoretical foundation for its potential as a target in immunotherapy. Declarations Data availability Data is provided within the manuscript or supplementary information files. Clinical trial number Not applicable. Acknowledgements We are grateful to the investigators who shared the GWAS data. Funding Not applicable. Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution G.L.: Conceived and designed the study, performed data analysis, interpreted the results, and wrote the manuscript. Led the Mendelian randomization analysis, including data processing and statistical evaluation.Y.Z.: Collected and organized part of the data, participated in the implementation of MR analysis, contributed to writing the results section, and helped revise the manuscript.R.W.: Participated in designing the study, gathering and integrating data, and creating the analysis framework. Helped interpret data and contributed to writing and revising various parts of the manuscript.L.S.: Conducted the literature review, integrated relevant data, assisted in data analysis, and contributed to the initial draft of the manuscript.Z.X.: Provided software support, prepared figures, and assisted in manuscript revision.B.C.: Provided overall project supervision, contributed to study design and data interpretation, particularly in the selection of analytical methods and statistical models. Provided academic guidance and contributed to manuscript revision. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.xlsx 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-7128416\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":498925344,\"identity\":\"cdab0a08-5c28-4eb7-af9c-440e664a7409\",\"order_by\":0,\"name\":\"Gengchen Lu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong University of Traditional Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gengchen\",\"middleName\":\"\",\"lastName\":\"Lu\",\"suffix\":\"\"},{\"id\":498925346,\"identity\":\"a27c71f5-7bc8-434d-b779-b2b390622bc0\",\"order_by\":1,\"name\":\"Yining 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12:57:16\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32365,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVenn plot of eQTls and palmitoylation genes\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/444524b1f63147a53065394a.png\"},{\"id\":88895934,\"identity\":\"32fcde84-ad26-4122-aaa4-501fa5a110e5\",\"added_by\":\"auto\",\"created_at\":\"2025-08-12 13:05:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49362,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eForest plot of palmitoylated genes associated with rectal cancer\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/c6e8034e82719ebf78c2f461.png\"},{\"id\":88895936,\"identity\":\"8fa7e5d1-4ce1-4d1c-8c48-8ee32c333b98\",\"added_by\":\"auto\",\"created_at\":\"2025-08-12 13:05:16\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":27085,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAnalysis of expression differences in the GEO database\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/4b389c7d232d54076f6ad585.png\"},{\"id\":88893658,\"identity\":\"e4253ab4-6ee4-4246-b487-734efa9d0988\",\"added_by\":\"auto\",\"created_at\":\"2025-08-12 12:57:16\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":395599,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eForest plot of immune cells associated with rectal cancer\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/5c7eb8c689ec4b477e995ea7.png\"},{\"id\":88893660,\"identity\":\"20e32b04-9741-406c-924e-543610f4b6a6\",\"added_by\":\"auto\",\"created_at\":\"2025-08-12 12:57:16\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":316581,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eForest plot of intestinal microbiota associated with rectal cancer\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/20629ba6573a2f542add877a.jpg\"},{\"id\":91616777,\"identity\":\"b7c6bc27-3b58-4bd0-ba85-e557d5db24da\",\"added_by\":\"auto\",\"created_at\":\"2025-09-18 10:40:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1951188,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/2ed2ef36-dead-483c-9f7f-6152ede56ec1.pdf\"},{\"id\":88893653,\"identity\":\"ad6eda67-c6a3-461e-9cd3-8ae8de1ac549\",\"added_by\":\"auto\",\"created_at\":\"2025-08-12 12:57:16\",\"extension\":\"xlsx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16730,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7128416/v1/eacea03b92211c942a6f6bff.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"PPT2 Promotes the Rectal Cancer via CD4+ Immune Cells\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eRectal malignant tumor is a common cancer of the digestive system, and is characterized by high incidence, high lethality, and high recurrence rate, posing a serious threat to human health. The pathogenesis of rectal malignant tumor is complex and is affected by multiple factors such as genetics, lifestyle, dietary structure and inflammatory state. The process of rectal cancer is closely related to the changes in the composition and diversity of the intestinal microbiota, and the intestinal microbiota can cause tumor occurrence and development by affecting the immune function and metabolic process of the host [1\\u0026ndash;3]. In addition, abnormal immune function is also a crucial factor leading to rectal cancer. Imbalance in the regulation of the immune system in chronic inflammatory states may lead to tumor occurrence and development. The interaction between intestinal microbiota and immune cells is crucial for tumor immunosurveillance, with its dysregulation potentially facilitating rectal carcinogenesis [4\\u0026ndash;6].\\u003c/p\\u003e\\u003cp\\u003eProtein post-translational modifications (PTMs) refer to covalent binding of small chemical molecular groups to amino acid side chains of proteins via enzymatic reactions after the protein-synthesis is finished, thus modifying protein structure and function [7]. Recent studies have increasingly revealed the crucial role of protein PTMs in regulating tumor occurrence and development. In tumor biology, PTMs are involved in tumor occurrence, development, invasion, and metastasis by affecting biological processes such as signal transduction, cell cycle, metabolic reprogramming and immune responses[8]\\u003c/p\\u003e\\u003cp\\u003ePalmitoylation is a significant form of reversible lipid modification and widely exists in a variety of proteins within cellular environments. Palmitoylation regulates the subcellular localization, stability and functional state of proteins by binding palmitoyl groups to cysteine residues of proteins to form thioester bonds. This modification plays a crucial role in numerous biological processes, including signal transduction, the transport and localization of membrane proteins, and interactions between protein and other molecules[9, 10].\\u003c/p\\u003e\\u003cp\\u003eIn the field of cancer research, it is found that palmitoylation is significantly associated with tumor occurrence and immunotherapy. Palmitoylation can affect immune evasion and drug tolerance mechanisms of tumor cells by means of T cell activation, cytokine signal transduction as well as autophagy and phagocytosis[11]. Besides, palmitoylation abnormality can affect immune cell function and intestinal microbial composition and thus can further affect immune surveillance and the development of tumors. [12, 13]. Palmitoylation is involved in tumor occurrence and development of rectal cancer by affecting cell signaling pathways and protein function [14]. Palmitoylation affects intestinal inflammation and barrier function by regulating the palmitoylation and phosphorylation of signal transduction and activator of transcription 3 (STAT3) [15]. These findings indicate that palmitoylation plays a crucial role in the occurrence and development of rectal cancer by regulating immune responses and intestinal barrier function. In summary, although the role of palmitoylation in immune signal transduction, inflammatory response and intestinal barrier regulation has been partially revealed, its specific mechanism in rectal cancer still requires further study.\\u003c/p\\u003e\\u003cp\\u003eMendelian randomization (MR) is an epidemiological research method based on genetic variation, which is widely used to assess causal relationships between traits and diseases with significant advantages [16]. Genome-Wide Association Study (GWAS) is a scientific approach that identifies genetic variants associated with diseases or traits by analyzing single nucleotide polymorphisms (SNP) associated with phenotypic traits. In addition, the integration of multi-omics data, encompassing gene expression and epigenetic modifications (e.g., DNA methylation), can facilitate the identification of expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL), and thereby enhance our understanding of the function of genetic variants [17, 18]. Compared with conventional observational studies, MR analysis can simulate the study design of a Randomized Controlled Trial (RCT), which is more reliable in inferring causality. Furthermore, MR analysis can effectively reduce the interference of confounding factors and avoid the bias caused by reverse causality [19].\\u003c/p\\u003e\\u003cp\\u003eeQTL which affects the genetic variants of gene expression level or SNP can regulate the expression of specific genes in the genome of an individual [20]. The study of eQTL can facilitate the acquisition of insights into the genetic factors that regulate gene expression, and thereby further reveal the mechanisms that link the function of genes to expression traits [21]. Considering the potential relationship between palmitoylation in the field of immune regulation and fields of gut microbial function and oncology, this study, using multiple statistical genetic approaches, aims to systematically investigate the mechanism of palmitoylation-related genes in regulating the intestinal flora, immune cellular composition and their combined effects on rectal cancer, expecting to provide new targets and strategies for disease treatment.\\u003c/p\\u003e\"},{\"header\":\"2 Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Research design\\u003c/h2\\u003e\\u003cp\\u003eThis study investigates the mechanistic role of palmitoylation genes in the development and progression of rectal cancer through a multi-stage analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). First, known palmitoylation genes were extracted through literature search and intersected with filtered eQTL data to identify palmitoylation genes associated with rectal cancer. Then, expression differential analysis was conducted using the GEO database to validate and determine the target genes. Mendelian randomization (MR) methods were used to explore the causal relationships between immune cells, gut microbiota, and rectal cancer. Finally, MR analysis results were associated with the target genes, and a mediation analysis was performed to quantify the effect proportion of the target genes in the immune cells and gut microbiota influencing the progression of rectal cancer, revealing the potential mediating role of palmitoylation genes.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Literature Search Strategy and Palmitoylation Gene Collation\\u003c/h2\\u003e\\u003cp\\u003eRelevant literature was searched through databases (e.g., PubMed) for palmitoylated genes published before the cut-off date. Searches were conducted using the following keywords: 'palmitoylation' and 'gene'. The inclusion criteria for the literature were as follows: (1) Strong evidence supporting the authenticity of the palmitoylated genes; (2) Information including the first author, year of publication, country or region, and type of study; (3) Authenticity of the article with an impact factor of 2 or higher. The exclusion criteria were: (1) Palmitoylated genes not clearly named; (2) Genes that could not be clearly categorized or were ambiguous; (3) Case reports or conference abstracts.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 eQTL Dataset\\u003c/h2\\u003e\\u003cp\\u003eThe eQTL data of all genes in this study were obtained from the eQTLGen database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://eqtlgen.org\\u003c/span\\u003e\\u003cspan address=\\\"https://eqtlgen.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). All data were cis-eQTL (cis-eQTL), derived from blood samples. The instrumental variables (SNPs) were subjected to screening according to the following parameters: p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;5e-8, clumping window :10000kb, R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1. This process resulted in the identification of 15,695 genes with cis-eQTL data.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Endings dataset\\u003c/h2\\u003e\\u003cp\\u003eRectal malignancy data were obtained from the FinnGenc R12 database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_R12_C3_RECTUM_EXALLC.gz\\u003c/span\\u003e\\u003cspan address=\\\"https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_R12_C3_RECTUM_EXALLC.gz\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe disease identification code was C3_RECTUM_EXALLC, which included a total of 4,844 rectal cancer cases and 378,749 controls. All participants were from the European population.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5 Two-sample Mendelian randomisation analysis\\u003c/h2\\u003e\\u003cp\\u003eMR uses genetic variation to represent risk factors, and the genetic instrumental variables (IVs) used in the analyses must satisfy three key assumptions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)[22]: (1) The instrumental variables are significantly associated with the exposure factors. (2) The instrumental variables are independent of any confounders of the outcome association. (3) The instrumental variables affect the outcome only through their influence on the exposure factors and not by other means.\\u003c/p\\u003e\\u003cp\\u003eThe two-sample Mendelian randomisation analysis was conducted in this study by utilising the TwoSampleMR package. The cis-eQTL data corresponding to the palmitoylation gene was used as the exposure, and rectal cancer was used as the outcome. In order to reduce the bias caused by weak instrumental variables, instrumental variables with F values less than 10 were excluded from the analysis. In this study, the primary algorithm employed was the inverse variance weighted (IVW) method, with the objective of excluding exposures for which it was not possible to calculate IVW outcomes. The robustness of the results was assessed using the MR-Egger intercept test to detect multiplicity, Cochran's Q to detect heterogeneity, and sensitivity analysis using the leave-one-out method.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.6 Expression difference analysis\\u003c/h2\\u003e\\u003cp\\u003eRectal cancer microarray data were obtained from the Gene Expression Omnibus (GEO) database, dataset ID GSE75970, which contains a total of 32 diseased tissues and 96 normal tissues, for a total of 128 samples. Differential expression analysis was performed using the limma package, and genes were considered to be differentially expressed if they met the following criteria: disease tissue was higher or lower than normal tissue, and the p-value was less than 0.05.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.7 Intermediary analysis\\u003c/h2\\u003e\\u003cp\\u003eWe used two-step MR mediator analysis to explore whether palmitoylated genes play a role in promoting disease by regulating immune cells and gut flora. Immune cell data (data ID: ebi-a-GCST90001391- ebi-a-GCST90002121) and gut flora data were downloaded from the IEU OpenGWAS project (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://gwas.mrcieu.ac.uk/\\u003c/span\\u003e\\u003cspan address=\\\"https://gwas.mrcieu.ac.uk/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Instrumental variables (SNPs) were screened according to the following parameters: p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;5e-8, clumping window :10000kb, r\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1, and finally 612 immune cell data and 473 intestinal flora data were obtained. First, calculate the effect size of palmitoylated gene eQTL to rectal cancer, labeled as BetaT; then calculate the effect size of palmitoylated gene eQTL to immune cells and intestinal flora, labeled as BetaA, and the effect size of immune cells and intestinal flora to rectal cancer, labeled as BetaB. Finally, calculate the mediator effect (BetaAB): BetaA*BetaB, the Mediated proportion (P\\u003csub\\u003ebetaAB\\u003c/sub\\u003e):(BetaAB/BetaT) *100%. Results in which the mediating effect was not in the same direction as the total effect were excluded, and results with a mediating effect proportion greater than 20% were selected for analysis.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.8 Statistical methods\\u003c/h2\\u003e\\u003cp\\u003eAll statistical analyses in this study were performed using the opensource free R software (version 4.4.2), and the study was conducted using R packages such as \\u0026ldquo;Mendelian Randomization\\u0026rdquo; and \\u0026ldquo;MRPRESSO\\u0026rdquo;, Mendelian Randomization\\u0026rdquo;, \\u0026lsquo;MRPRESSO\\u0026rsquo;, \\u0026lsquo;TwoSampleMR\\u0026rsquo; and other R packages were used for the analysis. PLINK software (version v1.90) was used to perform the local removal of chain imbalances. The threshold of statistical significance was set at p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 Palmitoylation gene-eQTL\\u003c/h2\\u003e\\u003cp\\u003eA retrospective analysis of the palmitoylation literature was conducted to identify 31 palmitoylation genes that have been confidently associated[13, 23, 24]. These genes were then filtered to identify 22 co-located genes by taking intersections with the 15,695 eQTL data obtained after local elimination of linkage disequilibrium (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Palmitoylation eQTL gene- Rectal cancer\\u003c/h2\\u003e\\u003cp\\u003eThe identified 22 palmitoylated eQTL genes with rectal cancer were subjected to batch Mendelian randomization analysis, and a total of three loci were obtained by extracting the P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 results based on the IVW results and visualizing the expression in forest plots (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 Differential Expression Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe above genes were validated by differential expression analysis and visualized for expression (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The discrepancy between the findings was found to be statistically significant, with the odds ratio (OR) value meeting the 95% confidence interval (CI) requirement. Furthermore, all the OR values obtained with eQTL were found to be consistent with each other. Consequently, PPT2, ZDHHC14 and ZDHHC20 were identified as the final target genes (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), with PPT2(OR\\u0026thinsp;=\\u0026thinsp;1.2244, 95% CI\\u0026thinsp;=\\u0026thinsp;1.0544\\u0026ndash;1.4217, P\\u0026thinsp;=\\u0026thinsp;0.0079) and ZDHHC20(OR\\u0026thinsp;=\\u0026thinsp;1.1322, 95% CI\\u0026thinsp;=\\u0026thinsp;1.0165\\u0026ndash;1.2611, P\\u0026thinsp;=\\u0026thinsp;0.0239) being classified as risk factors for rectal cancer and ZDHHC14(OR\\u0026thinsp;=\\u0026thinsp;0.9238, 95% CI\\u0026thinsp;=\\u0026thinsp;0.8726\\u0026ndash;0.9807, P\\u0026thinsp;=\\u0026thinsp;0.0094) as a protective factor.\\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 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char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eID\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNsnp\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMethod\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eB\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eSE\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eOR (95%CL)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003ePvalue\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePPT2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eIVW\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.2024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0762\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.2244(1.0544\\u0026thinsp;~\\u0026thinsp;1.4217)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.0079\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eZDHHC14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eIVW\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.0793\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0305\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.9238(0.8702\\u0026thinsp;~\\u0026thinsp;0.9807)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.0094\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eZDHHC20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eIVW\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1242\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0550\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.1322(1.0165\\u0026thinsp;~\\u0026thinsp;1.2611)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.0239\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.4 Rectal cancer-associated immune cells and intestinal flora\\u003c/h2\\u003e\\u003cp\\u003eScreening of immune cells and intestinal flora associated with rectal cancer: a batch two-sample Mendelian randomization study with immune cells and intestinal flora as exposure and rectal cancer as outcome, respectively, yielded a total of 27 immune cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) and 29 intestinal microbiota (Fig.\\u0026nbsp;6) associated with rectal cancer.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;6 Forest plot of intestinal microbiota associated with rectal cancer\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.5 Target genes-associated immune cells and intestinal flora\\u003c/h2\\u003e\\u003cp\\u003eConduct MR analysis of the target genes with the above-mentioned immune cells and intestinal microbiota, and perform mediation analysis by integrating all the results (Supplementary material). Based on the results of screening the data in the table for a mediating effect proportion of not less than 20% (P\\u003csub\\u003ebetaAB\\u003c/sub\\u003e \\u0026ge; 20%), it can be preliminarily concluded that PPT2 promotes rectal cancer through CD127 on CD45RA\\u0026thinsp;+\\u0026thinsp;CD4+ (P\\u003csub\\u003ebetaAB\\u003c/sub\\u003e = 20.66%) FSC-A on CD4+ (P\\u003csub\\u003ebetaAB\\u003c/sub\\u003e = 29.96%) and SSC-A on CD4+ (P\\u003csub\\u003ebetaAB\\u003c/sub\\u003e = 24.86%) mediated effects promote rectal carcinogenesis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\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\\u003eResults of the downstream mediation analysis of PPT2 after screening\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\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=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eImmune cell phenotype\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBetaT\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eBetaA\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eBetaB\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eBetaAB\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eSE\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eZ\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eP\\u003csub\\u003ebetaAB\\u003c/sub\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCD127 on CD45RA\\u0026thinsp;+\\u0026thinsp;CD4+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.2024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.4599\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.0909\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0418\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.0645\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.6482\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e20.66%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFSC-A on CD4+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.2024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.3465\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.1750\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0606\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.0358\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.6916\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e29.96%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSSC-A on CD4+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.2024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.3666\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.1373\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.0503\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.0375\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.3414\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e24.86%\\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\\u003eBetaT: The total effect of PPT2 on rectal cancer; BetaA: The effect of PPT2 on immune cells; BetaB: The effect value of immune cells on rectal cancer; BetaAB: The mediating effect, that is, the introduced effect, which represents the effect of PPT2 on rectal cancer mediated by immune cells; SE is the standard error; Z is the statistic; P\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003eBetaAB\\u003c/b\\u003e\\u003c/sub\\u003e \\u003cb\\u003eis the proportion of the mediating effect.\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4 Disscusion\",\"content\":\"\\u003cp\\u003eIn this study, we employed two-sample Mendelian randomization to reveal the potential impact of palmitoylation-related genes on the development of rectal cancer. Further mediation analysis indicated that PPT2 may promote rectal cancer initiation by modulating T cell activation phenotypes.\\u003c/p\\u003e\\u003cp\\u003ePPT2 (Palmitoyl-Protein Thioesterase 2) is a member of the depalmitoylase family, and its main function is to catalyze the hydrolysis of palmitoyl groups on the surface of proteins and to regulate the membrane localization, stability, and signaling behavior of proteins. Depalmitoylases are involved in regulating the lipid composition and dynamics of cell membranes, which is essential for maintaining the integrity and function of cell membranes [25, 26]. In addition, depalmitoylase may affect cell metabolism and growth by regulating membrane anchoring of proteins and signaling[27]. In recent years more and more studies have begun to focus on the potential role of PPT2 in tumor immunomodulation, especially in the regulation of T cell function. It has been shown that PPT2 may influence the effect of immune response in the tumor microenvironment by regulating T cell function and survival, and this process may be closely related to its regulation of protein palmitoylation level [28, 29].\\u003c/p\\u003e\\u003cp\\u003eIn the present study, PPT2 significantly mediated rectal cancer risk through CD4\\u0026thinsp;+\\u0026thinsp;T cells. Among them, CD127 on CD45RA\\u0026thinsp;+\\u0026thinsp;CD4\\u0026thinsp;+\\u0026thinsp;was a risk factor for rectal cancer, while FSC-A on CD4\\u0026thinsp;+\\u0026thinsp;and SSC-A on CD4\\u0026thinsp;+\\u0026thinsp;were protective factors for rectal cancer, thus PPT2 promotes rectal cancer through bidirectional regulation of different phenotypes of immune cells. In tumor immunity, the role of CD4\\u0026thinsp;+\\u0026thinsp;T cells is not limited to the activation and proliferation of adjuvant CD8\\u0026thinsp;+\\u0026thinsp;T cells, but also includes direct involvement in the recognition and clearance of tumor cells. However, when the function of CD4\\u0026thinsp;+\\u0026thinsp;T cells is suppressed or transformed into regulatory T cells (Treg), they may inhibit anti-tumor immune responses and promote immune escape from tumors [30]. Studies have shown that tumor cells can induce a functional shift in CD4\\u0026thinsp;+\\u0026thinsp;T cells through multiple mechanisms, such as inhibiting CD4\\u0026thinsp;+\\u0026thinsp;T cell activity by upregulating the expression of immune checkpoint molecules PD-1 and PD-L1 [31].\\u003c/p\\u003e\\u003cp\\u003eCD127 (IL-7Rα) plays an important role in the tumor immune microenvironment, and its high expression is often associated with T cell over-activation, functional exhaustion, and immune tolerance, which plays an important role in tumor immunotherapy [32, 33]. It has been shown that CD127-overexpressing CD4\\u0026thinsp;+\\u0026thinsp;T cell subpopulations can form an \\u0026ldquo;immunopseudoquiescent\\u0026rdquo; state in tumor tissues, which not only makes it difficult to clear tumor cells, but also may be \\u0026ldquo;recruited\\u0026rdquo; by tumor cells as an immune barrier. Therefore, PPT2 may affect the membrane localization or internalization of CD127 by regulating the level of palmitoylation of T cell membrane proteins, thus promoting the formation of an immune tolerance phenotype in this subpopulation. Comparatively, FSC-A and SSC-A indexes are usually used to reflect T cell volume and cell granule complexity, which are important parameters for measuring the activation and functional status of T cells [34]. It has been shown that activated T cells play an important role in the immune microenvironment of rectal cancer and are closely related to tumor progression and prognosis[35\\u0026ndash;37]. Both are rectal cancer protective factors, suggesting that activated T cells may play a positive role in tumor clearance, and PPT2, by down-regulating these two parameters, may have weakened the anti-tumor effect of T cells at the functional level. Taken together, PPT2 may bi-directionally regulate different phenotypes of T cells through depalmitoylation modification, forming a favorable immune microenvironment for rectal cancer.\\u003c/p\\u003e\\u003cp\\u003eNotably, palmitoylation, as a dynamic and reversible lipid modification, possesses the ability to finely regulate cell signaling and membrane protein functions. It has been reported at several key nodes such as T cell receptor complexes, immune checkpoint proteins (e.g., PD-L1), and inflammatory signaling molecules (e.g., NOD2) [38, 39]. For example, inhibition of palmitoylation of PD-L1 enhances the immune response of T cells against tumors [40]. In addition, palmitoylation has been found to regulate the ability of intestinal epithelial cells to sense microbe-associated molecular patterns (MAMPs), a potential regulatory hub in host-microecology interactions [41, 42]. For example, this study also found that ZDHHC20 increases the risk of rectal cancer through UBA3282 sp002493835l (Mediated proportion\\u0026thinsp;=\\u0026thinsp;15.66%) and Prevotella sp900317685 (Mediated proportion\\u0026thinsp;=\\u0026thinsp;11.32%),moreover, ZDHHC14 was found to reduce the risk of rectal cancer through CAG-273 sp003534295 (Mediated proportion\\u0026thinsp;=\\u0026thinsp;18.85%) (Supplementary Material).Therefore, palmitoylated genes may not only be regulatory factors in rectal carcinogenesis, but also central to the interaction between host genetics, immune status and microcosmic environment. The regulatory mechanism proposed in this study provides a new perspective for understanding the integration of genetic background and external environmental factors in tumorigenesis and lays a theoretical foundation for the development of preventive or therapeutic strategies targeting the palmitoylation pathway in the future.\\u003c/p\\u003e\"},{\"header\":\"5 Limitation\",\"content\":\"\\u003cp\\u003eSeveral limitations of the present study remain. First, the genetic and phenotypic data used in this study were mainly derived from European populations, especially represented by the Finnish population, which may have an ethnostructural bias that limits the applicability of the findings to other populations. Therefore, future validation in a wider range of ethnic and regional populations is necessary to improve the generalizability and extrapolation of the findings. Second, although this study initially revealed the potential mechanism of PPT2 in rectal carcinogenesis through two-sample Mendelian randomization and mediation analysis, it lacked direct validation of their regulatory effects and molecular mechanisms in ex vivo experiments (e.g., cellular function experiments or animal models), especially the identification of palmitoylated modification sites and their functional impacts still need to be explored in depth. In addition, the study failed to fully incorporate potential interactions between environmental factors (e.g., diet, inflammatory state, or intestinal barrier disruption) and palmitoylation regulation, which may play key roles in immune regulation and changes in the gut flora structure, and future studies may combine epigenetic and environmental exposure data for integrated analysis\\u003c/p\\u003e\"},{\"header\":\"6 Conclusion\",\"content\":\"\\u003cp\\u003eIn this study, two-sample Mendelian randomization and mediation analysis were employed to systematically investigate the potential mechanisms by which palmitoylation-related genes contribute to the development of rectal cancer. The results demonstrated that high expression of the PPT2 gene significantly increased the risk of rectal cancer, with its pathogenic effect partially mediated by changes in the phenotype of CD4⁺ T-cell subpopulations. Specifically, PPT2 may promote tumor immune escape by modulating the functional state of CD127 high-expressing T-cell subsets. Additionally, PPT2 might impair the anti-tumor activity of T cells by downregulating activation-associated parameters such as FSC-A and SSC-A. Robustness analyses using multiple statistical models confirmed the reliability of these findings.\\u003c/p\\u003e\\u003cp\\u003eThis study is the first to elucidate, from a genetic causal perspective, the link between palmitoylation modification, immune regulation, and tumorigenesis. It provides new insights into the role of PPT2 in shaping the tumor immune microenvironment and offers a theoretical foundation for its potential as a target in immunotherapy.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData is provided within the manuscript or supplementary information files.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe are grateful to the investigators who shared the GWAS data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics declarations \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests \\u0026nbsp;\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eG.L.: Conceived and designed the study, performed data analysis, interpreted the results, and wrote the manuscript. Led the Mendelian randomization analysis, including data processing and statistical evaluation.Y.Z.: Collected and organized part of the data, participated in the implementation of MR analysis, contributed to writing the results section, and helped revise the manuscript.R.W.: Participated in designing the study, gathering and integrating data, and creating the analysis framework. Helped interpret data and contributed to writing and revising various parts of the manuscript.L.S.: Conducted the literature review, integrated relevant data, assisted in data analysis, and contributed to the initial draft of the manuscript.Z.X.: Provided software support, prepared figures, and assisted in manuscript revision.B.C.: Provided overall project supervision, contributed to study design and data interpretation, particularly in the selection of analytical methods and statistical models. Provided academic guidance and contributed to manuscript revision.\\u003c/p\\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\":\"info@researchsquare.com\",\"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\":\"Rectal Cancer, Palmitoylation, PPT2, Immune Cells, Mendelian Randomization\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7128416/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7128416/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRecent studies have revealed the importance of protein palmitoylation in controlling immune responses and sustaining the balance of intestinal flora. Furthermore, this process is associated with a variety of tumor diseases. However, the mechanisms by which palmitoylation-related genes affect rectal cancer via immune or microecological pathways have not been systematically studied.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study uses Mendelian randomization and mediation analysis to explore the causal interactions and potential mechanisms between palmitoylated genes and immune cell phenotypes, intestinal flora and rectal cancer. Based on two-sample MR analysis and mediation analysis, this study integrates genome-wide association study data of rectal cancer from FinnGen database, immune cell phenotype data from INTERVAL project and GWAS data of intestinal flora from MiBioGen. The study firstly evaluated the causal relationship between palmitoylation-related genes and rectal cancer risk, and then further explored the mediating mechanisms of immune cell characteristics and intestinal flora using mediation analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe results showed that high expression of the PPT2 gene significantly increased the risk of rectal cancer, and its pathogenic effect was mediated partly through the CD4+ T-cell phenotype. Mediation analyses revealed that the regulatory effect of PPT2 on CD127 high-expressing T-cell subsets (Mediated proportion= 20.66%) may promote immune escape. Alternatively, it may weaken the anti-tumor function of T cells at the functional level by downregulating the expression of FSC-A (Mediated proportion = 29.96%) and SSC-A (Mediated proportion = 24.86%). The results of the correlation analysis showed good robustness in multiple statistical models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe present study reveals the regulatory role of palmitoylation modification in immune regulation, clarifies the potential mechanism of PPT2 in rectal carcinogenesis, and suggests that palmitoylation-related genes can be used as early warning biomarkers and potential research directions for intervention targets.\\u003c/p\\u003e\",\"manuscriptTitle\":\"PPT2 Promotes the Rectal Cancer via CD4+ Immune Cells\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-12 12:57:11\",\"doi\":\"10.21203/rs.3.rs-7128416/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"19dcb4ae-71c6-48e7-8647-ccf1b1097e2d\",\"owner\":[],\"postedDate\":\"August 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-18T10:39:49+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-12 12:57:11\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7128416\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7128416\",\"identity\":\"rs-7128416\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}