CRC genome-driven metabolic reprogramming and immune microenvironment remodeling

CRC genome-driven metabolic reprogramming and immune microenvironment remodeling | 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 CRC genome-driven metabolic reprogramming and immune microenvironment remodeling Yan Xiong, Weiqiang Xiong, Yanhua Wang, Chuan He, Yimei Zhan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4090131/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 Colorectal cancer is a global digestive tract malignancy closely tied to microsatellite instability (MSI). MSI stems from DNA mismatch repair issues, categorized as MSI-High (MSI-H), MSI-Low (MSI-L), or Stable (MSS). Tailoring treatments based on MSI status is vital. MSI-H tumors, with high mutation and neoantigen loads, respond well to immune checkpoint inhibitors (ICIs). However, some MSI-H tumors display resistance due to complex factors like the tumor microenvironment, signaling pathways, immune cells, and checkpoint molecules. Methods Through the analysis of CRC genomic data, we identified the key genomic events that drive MSI. At the same time, through transcriptome analysis, we discovered the key genes. Results We performed a differential analysis between MSI-H and MSS/MSI-L and found that signaling pathways involved in lipid and hormone metabolism were significantly inhibited, including cholesterol homeostasis and hormone metabolism processes. At the same time, immune-related pathways were significantly activated. We identified genes associated with MSI-H, such as FAT4, BRAF, APC, and TTN, that were mutated at a higher frequency and number in MSI-H patients, thereby affecting tumor initiation, progression, and treatment response. These genes participate in different signaling pathways, such as Wnt/β-catenin pathway, MAPK pathway, PI3K/AKT pathway, etc. Conclusion This study reveals the presence of an active immune response in MSI-H tumors along with reduced levels of lipid metabolism and abnormal pathway phenotypes related to the proliferation and migration of Wnt/β-catenin and the MAPK pathway. Colorectal cancer microsatellite instability metabolic reprogramming immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Colorectal cancer is a common malignant tumor of the digestive tract, with high incidence and mortality worldwide[ 1 ]. The occurrence and development of colorectal cancer are closely related to microsatellite instability (MSI)[ 2 , 3 ]. MSI refers to the phenomenon of microsatellite sequence length changes during DNA replication due to defective DNA mismatch repair (MMR) system, which can be classified into high-frequency (MSI-H), low-frequency (MSI-L), and stable (MSS) types[ 4 ]. MSI testing is of significant clinical importance for the diagnosis, treatment, and prognosis of colorectal cancer, and currently, there are mainly two methods: molecular biology and immunohistochemistry[ 5 – 7 ]. Therefore, selecting appropriate treatment strategies based on MSI status is crucial for improving patients' quality of life and extending survival. MSI-H tumors have a high mutation burden and high neoantigen load, which can induce immune cell infiltration and immune response, thus showing good sensitivity to immune checkpoint inhibitors (ICI)[ 8 , 9 ]. In recent years, immunotherapy, especially immune checkpoint blockade (ICB), has become an innovative approach and has shown remarkable therapeutic effects in various cancer types such as melanoma, renal cancer, and lung cancer[ 10 , 11 ]. However, MSI-H tumors also exhibit certain heterogeneity, leading to some patients being resistant or unresponsive to ICI treatment[ 12 ]. The relationship between MSI-related immune cell infiltration and immune therapy is a complex process, involving various factors, such as the tumor microenvironment, intracellular signaling pathways of tumor cells, immune cell subsets, immune checkpoint molecules, tumor-associated antigens, etc[ 13 ]. In our research, we focused on exploring the genomic and transcriptomic characteristics of different MSI samples in CRC. We discovered driver genomic events and further analyzed the transcriptomic features of MSI-H samples. The metabolic reprogramming of MSI-H was identified, and we explored the characteristics of higher immune infiltration in MSI-H patients. Moreover, through machine learning algorithms, we analyzed the driver genes in MSI-H samples with high immune infiltration and compared their molecular features. Our study provides new insights into immunotherapy targeting MSI-H in CRC. Materials and Methods Colorectal Cancer Dataset and Data Processing RNA-Seq data of 401 COAD tumor samples, including TPM (Transcripts Per Million) data and counts data, along with corresponding clinical features, were downloaded from The Cancer Genome Atlas (TCGA) website ( https://portal.gdc.cancer.gov/projects/TCGA-COAD ). Ensembl IDs were converted to official gene symbols, and low-expressed genes were further analyzed. CRC Genomic Variation Analysis We downloaded CRC somatic mutation data using the R package "maftools" ( https://cran.r-project.org/web/packages/maftools/index.html )[ 14 ]. Subsequently, we plotted the mutation landscape for all CRC patients and identified differentially mutated genes between MSI-H and MSI-L. Additionally, we computed and compared the tumor mutation burden (TMB) between MSI-H and MSI-L. To visualize copy number variations in MSI(H/L) patients, we utilized bar plots. Evaluation of Immune Cell Infiltration In this study, the R package "xCell" was utilized to assess the landscape of immune cell infiltration in MSI-H and MSI-L patients. "xCell" is a gene signature-based method that combines gene set enrichment and deconvolution techniques to analyze microarray and RNA-seq expression profiles[ 15 ]. This method has the capability to predict the abundance of 64 cell types, including immune cells, hematopoietic cells, and epithelial cells. Additionally, we employed the ssGSEA method to estimate the infiltration levels of 28 immune cell subtypes in each sample, encompassing B cells, CD4 + T cells, CD8 + T cells, NK cells, neutrophils, macrophages, and more. To further classify COAD samples into distinct subgroups, we utilized the "pheatmap" R package with default parameters for clustering, which stratified all COAD samples into two subtypes based on the pattern of immune cell infiltration, namely, the high immune subtype and the low immune subtype. Mutation Data Processing To validate the mutation information of different immune subtypes in MSI-H, we downloaded somatic mutation data in maf format based on the hg19 reference genome from TCGA. We utilized the "oncoplot" function in the R package "Maftools" to generate an oncogenic plot, displaying the mutation status of the top 20 driver genes with the highest alternate allele frequency in each sample. Additionally, we employed the "mafCompare" function to compare the differences in driver genes between different subtypes and visualized the results as frequency bar plots. Differential Expression Analysis The COAD samples were divided into three groups: MSI-H, MSI-L, and MSS groups. DESeq2 was used for differential expression analysis, comparing the MSI-H group with the other two groups. The criteria for differential expression were set as |log2(fold change)| ≥ 1 and False Discovery Rate (FDR) ≤ 0.05. Identification of Key Genes Associated with MSI-H and Immune Infiltration Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify key prognostic genes associated with the MSI-H group in colorectal cancer samples. First, an appropriate soft threshold was chosen to transform the Adjacency Matrix (AM) into the Topological Overlap Matrix. Then, the correlation between gene consensus modules and MSI status, as well as immune infiltration, was analyzed. Modules that were significantly correlated with MSI-H and immune infiltration were selected for further analysis. Key genes were screened based on a correlation greater than 0.8 between genes and modules. Functional Enrichment Analysis The 'clusterProfiler' R package was used for functional annotation of differentially expressed genes and module genes based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Functional enrichment pathways were selected based on adjusted p-values ≤ 0.05, indicating significant enrichment. Survival Analysis The key genes selected from the relevant modules were subjected to survival analysis of colorectal cancer patients using the GEPIA (Gene Expression Profiling Interactive Analysis) (cancer-pku.cn) web tool. Prognostic Model Establishment Regression analysis was performed using the "survival" R package to screen potential prognostic genes among the brown module genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was applied to these prognostic candidate genes. Finally, the best prognostic model containing eight genes was established by selecting the optimal penalty parameter λ associated with the minimum 10-fold cross-validation. Results Key genomic events associated with different MSI phenotypes in CRC To explore the genetic mutation characteristics of MSI (H/L) patients in colorectal cancer (CRC), we used the R package "maftools" to download CRC somatic mutation data and generated mutation plots for all CRC patients (Fig. 1 -A). We found significant differences in gene mutations between MSI (H/L) patients and the general CRC population, with MSI-H patients showing higher mutation frequencies and quantities (Fig. 1 -B). Further comparison revealed 84 differentially mutated genes in MSI-H samples, most of which were specific to MSI-H. We listed the top 10 genes with the highest mutation frequencies (Fig. 1 -C). Additionally, we calculated the tumor mutation burden (TMB), and the results indicated that MSI-H samples had a higher TMB level (Fig. 1 -D and E). Furthermore, we analyzed copy number variations in MSI (H/L) patients, and the figure showed that PADI1, CCT6P3, SEMA3E, RNA5SP251, SLC35G5, and VPS37A tended to have more copy number variations in MSI-L samples (Fig. 1 -F). This further underscores the presence of distinct genomic variations in MSI (H/L) patients with CRC. Characterization of the transcriptional profile of MSI-H patients Colorectal cancer patients with dMMR/MSI-H tumors are significantly more sensitive to Immune Checkpoint Inhibitors (ICIs) than those with MSS)/MSI-L tumors. To explore the key molecular pathways that drive the regulation of the immune microenvironment in MSI-H tumors, we downloaded data from 456 colon cancer samples from the TCGA database ( https://portal.gdc.cancer.gov/repository ). First, we grouped the samples based on the patients' clinical phenotypes into Microsatellite High Instability (MSI-H), Microsatellite Low Instability (MSI-L), and Microsatellite Stable (MSS) groups. Then, we performed differential gene analysis between MSI-H and MSI-L as well as MSS samples, identifying 1748 and 1904 differentially expressed genes, respectively (Fig. 2 A, B). We conducted GO and KEGG database enrichment analysis on the upregulated and downregulated genes among the differentially expressed genes. We found that downregulated genes in the comparison between MSI-H and MSI-L were significantly enriched in signal pathways related to substance transport, such as organic anion transport, lipid transport, and sterol transport pathways. They were also enriched in hormone and metabolism-related pathways, including hormone level regulation, steroid metabolic process, and hormone metabolic process (Fig. 2 B, C). On the other hand, upregulated genes were significantly enriched in signal pathways related to immune cell recruitment and activation, such as interferon-gamma response pathway, leukocyte-mediated immunity, and positive regulation of T cell activation (Fig. 2 C, E). Interestingly, the enrichment results of differentially expressed genes between MSI-H and MSS were almost identical to the previous results (Fig. 2 D,F). This suggests that MSI-H is the major contributor to tumor-independent subtyping. In conclusion, these results indicate that the immune-related pathways driven by MSI-H tumor cells simultaneously inhibit substance transport signal pathways. Identification of Key MSI-H-Related Modules by WGCNA To identify key genes related to the MSI-H phenotype and high immune infiltration, we first performed hierarchical clustering on the 456 colon cancer samples and removed outlier samples through branch trimming (Fig. 3 ). Then, we used the WGCNA method to construct a weighted correlation network based on the remaining samples to identify gene modules most correlated with MSI-H and high immune infiltration. We chose 4 as the soft thresholding parameter to construct the scale-free gene co-expression network and obtained a total of 25 gene modules. Among them, we found that the blue module had the highest correlation with the MSI-H phenotype (cor = -0.66, p ≤ 0.001) (Fig. 3 A-E).To analyze the main molecular pathways involved in the negative correlation between the blue module and MSI-H phenotype, we performed functional enrichment annotation of the genes within the module using the GO and KEGG databases (Fig. 3 F-G). GO annotation showed that the genes in the blue module were mainly related to substance transport, such as anion transport, organic hydroxy compound transport, and lipid transport signaling pathways. They were also associated with pathways related to cellular responses to foreign substances. KEGG annotation revealed that the blue module was related to digestion-related pathways, such as bile secretion and gastric acid secretion, as well as pathways associated with cell growth and differentiation, such as the Wnt signaling pathway, ErbB signaling pathway, and VEGF signaling pathway (Fig. 3 H, I). Molecular Features of High Immune Infiltration in MSI-H Immune cell infiltration is closely related to tumor metastasis. To explore the relationship between MSI and immune cell infiltration and its association with patient prognosis, we further divided the MSI-H samples into high immune infiltration and low immune infiltration phenotypes. We then performed correlation analysis between the modules and phenotypes (Fig. 3 H), and found that the brown module was most correlated with high immune infiltration (cor = -0.27, p ≤ 0.001). We conducted functional annotation analysis on the genes within the brown module using the GO database, which showed that the module was mainly involved in cellular molecular metabolic processes, chromatin activities in the cell nucleus including DNA repair, chromatin structure organization, transcriptional regulation, and protein localization (Fig. 3 I). We identified key genes within the module using the threshold screening method (module membership ≥ 0.8) and found 15 genes that met the criteria in the brown module. Subsequently, we used the LASSO model to construct a prognosis risk assessment model based on only 8 genes. The cvfit and lambda curve are shown in Fig. 4 A, B. We then plotted the survival curves of colon cancer patients with significant genes in the module and genes used to establish the prognosis risk assessment model using the online tool GEPIA. Five genes were found to be significantly associated with patient survival: COX72A, GED1, and MRPL22 were identified as protective genes associated with prolonged survival, while DNAH1 and FAM178A were identified as risk genes associated with shortened survival (Fig. 4 D-H). MSI-H tumors show increased infiltration of immune cells To observe the changes in the tumor microenvironment in colorectal cancer (CRC), we employed the xCell algorithm to determine the landscape of immune cell infiltration in all COAD patients from the TCGA database (Fig. 5 A). It was observed that the MSI-H group exhibited more pronounced immune infiltration. Furthermore, we performed an in-depth analysis of specific immune cells in the MSI-H group. Using the ssGSEA algorithm, we downloaded marker genes for 28 immune cell types from the internet and assessed the immune infiltration levels in 79 MSI-H samples derived from TCGA-COAD. By clustering MSI-H patients using the R package "pheatmap," two distinct infiltration patterns were identified: high immune infiltration and low immune infiltration (Fig. 5 B), and the clustered samples were visualized in Fig. 5 C. Upon examining the differences in immune cell infiltration levels between high and low immune infiltration groups, significant variations were found in activated B cells, activated CD4 T cells, and activated CD8 T cells (Fig. 5 D). Molecular features of elevated immune cell infiltration in MSI-H To determine the differences in cancer-related gene mutations between the high immune infiltration and low immune infiltration groups, we first calculated the gene mutations in each group. Representative gene mutations in both groups are shown in Fig. 6 A-B. For MSI-H patients, examining the mutation patterns in low immune infiltration samples (Fig. 6 A) revealed that TTN (90%), MUC16 (74%), RNF43 (74%), BMPR2 (62%), and BRAF (62%) were the top five genes with the highest mutation frequencies. On the other hand, KMT2D (73%), RNF43 (73%), SYNE1 (73%), FAT4 (69%), and other genes exhibited higher mutation frequencies in MSI-H patients with high immune infiltration (Fig. 6 B). We compared the distribution of COAD gene mutations in the low and high immune infiltration subgroups, as shown in Fig. 6 C-D. We identified a total of 176 differentially mutated genes, with 63 genes showing a higher mutation frequency in the Low group, while the remaining 113 genes exhibited a higher mutation frequency in the High group. Functional enrichment analysis of the differentially expressed genes (Fig. 6 E-F) revealed significant enrichment in cell adhesion, antigen presentation, protein binding, as well as the regulation of channel activity and synaptic transmission pathways. Discussion The frequency of dMMR/MSI-H tumors in CRC is approximately 15%[ 2 ]. MSI refers to the phenomenon of microsatellite length changes caused by insertion or deletion mutations during DNA replication. MSI can be classified into MSI-H, MSI-L, and MSS based on the degree of instability, and MSI is a result of MMR protein dysfunction[ 16 ]. Tumors with MSI-H or dMMR features are generally more responsive to immune checkpoint therapy[ 17 – 19 ]. Pembrolizumab, a humanized IgG4 antibody, was the first immune checkpoint-targeted drug that showed promising results in patients with dMMR colorectal cancer[ 20 ]. Nivolumab, another PD-1 humanized monoclonal antibody, was approved by the FDA for the treatment of dMMR or MSI-H metastatic colorectal cancer in 2017[ 21 ]. MSI status can alter the tumor microenvironment (TME) of CRC patients in multiple ways, thus affecting the efficacy of ICIs in CRC patients. MSI-H CRC has more immune cell infiltration, which is associated with better prognosis. However, there is currently no systematic explanation of the differences in immune microenvironment among MSI subtypes[ 22 ]. In the differential analysis between MSI-H and MSS/MSI-L, we found that signal pathways related to lipid and hormone metabolism were significantly inhibited, including cholesterol homeostasis and hormone metabolic processes. Immune-related pathways were significantly activated, including leukocyte migration involved in inflammatory response and positive regulation of T cell chemotaxis. Some studies have shown that statin drugs can reduce the incidence of colon cancer[ 23 , 24 ]. However, another study suggested that the high cholesterol levels induced by statins can lower the risk of colorectal cancer. Additionally, they observed that a decrease in serum cholesterol levels within at least one year before cancer diagnosis is associated with an increased risk of colorectal cancer[ 25 ]. Obesity is also a potential factor that induces colon cancer, and obesity-related hormone metabolic disorders can cause macrophage polarization and cytokine expression, directly affecting tumor progression and survival in CRC patients [ 24 , 26 ]. In addition, it was also observed that the brown module in MSI-H is negatively correlated with high immune cell infiltration and contains key genes such as COX7A2, MRPL22, and KMT2C. COX7A2 is a subunit of cytochrome c oxidase (COX), which is a complex in the mitochondrial respiratory chain. While there is limited research on the relationship between COX7A2 and colon cancer, expression of COX7A1 is significantly downregulated in lung cancer patients, and it is closely associated with non-small cell lung cancer[ 27 ]. It is possible that COX7A2 may have a similar connection in colon cancer. MRPL22 encodes mitochondrial ribosomal protein L22, which is involved in the composition of mitochondrial ribosomes and participates in mitochondrial protein synthesis. Currently, research on MRPL22 in colon cancer is relatively limited. There is a report suggesting that LARP1-MRPL2 is a novel and recurrent fusion gene in non-Hodgkin B-cell lymphoma[ 28 ]. KMT2C encodes a protein called MLL3, which is a member of the histone methyltransferase (HMT) family responsible for adding methyl groups to the genome. KMT2C mutations are frequently observed in various cancers. that KMT2C inactivation may promote colorectal cancer development through transcriptional dysregulation[ 29 ]. Our analysis revealed the genomic characteristics of MSI (H/L) patients, with MSI-H patients exhibiting higher mutation frequencies. Previous research has indicated that MSI-H/dMMR (Microsatellite Instability-High/Deficient Mismatch Repair) and TMB-H (High Tumor Mutation Burden) reflect the high genomic mutation frequency, which may suggest the efficacy of immune checkpoint inhibitor therapy[ 30 ]. Furthermore, studies have shown that some CRC patients develop resistance to PD-1 inhibitors after treatment, with a subset presenting novel MMR defects or new microsatellite instability (nMMRD/nMSI). These novel defects lead to a decrease in tumor mutation burden (TMB) and neoantigen burden (TNB), thereby reducing tumor sensitivity to immune checkpoint inhibitors[ 31 ]. In our analysis, we identified FAT4, BRAF, APC, and TTN as genes associated with MSI-H, exhibiting higher mutation frequencies and numbers in MSI-H patients, thus influencing tumor initiation, progression, and treatment response[ 32 ]. These genes participate in different signaling pathways, such as the Wnt/β-catenin pathway, MAPK pathway, PI3K/AKT pathway, among others[ 33 , 34 ]. Overall, continuous research on the genomic characteristics of MSI (H/L) patients will lead to further discoveries and advancements in the understanding and treatment of colorectal cancer[ 35 ]. Our analysis also revealed differences in immune cell infiltration in MSI-H tumors. While previous studies have shown no significant difference in overall survival (OS) between MSI-H and MSS (microsatellite stable) patients[ 36 ], there are results suggesting a higher number of M0 macrophages and fewer plasma cells, as well as reduced CD8 + T cell infiltration, in tumors at later stages[ 10 ]. However, our findings indicate that MSI-H CRC patients exhibit varying degrees of infiltration for B cells, CD4 + T cells, and CD8 + T cells, suggesting changes in the tumor microenvironment for MSI-H patients. In our final analysis of the molecular characteristics of high immune cell infiltration in MSI-H tumors, we discovered that MSI-H CRC patients also exhibit certain mechanisms of immune evasion and resistance to treatment, such as T cell tolerance, T cell exclusion, and T cell exhaustion. These mechanisms may be associated with the enrichment of specific pathways in MSI-H CRC, including cell adhesion, antigen presentation, protein binding, as well as the regulation of channel activity and synaptic transmission. These findings are consistent with previous research and suggest potential targets for therapeutic interventions in MSI-H CRC patients[ 9 , 37 ]. Although we have already discussed some genes related to MSI and CRC, many genes have not been reported yet. More in vitro and in vivo experiments will be required in the future to validate the roles of these genes in CRC development. Additionally, there might be other omics alterations. For example, in a study by Wu Tao et al[ 38 ]. They conducted single-cell RNA sequencing on 23 MSI-H mCRC patients and found a significant decrease in the proportion of CD8 + T cells and a significant increase in M2 macrophages in the treatment-resistant group compared to the treatment-sensitive group. Furthermore, the team led by Fuchou Tang performed single-cell RNA sequencing[ 39 ], whole-genome sequencing, and multi-region whole-exome sequencing on 12 mCRC patients, discovering heterogeneity at the genomic, epigenetic, and transcriptomic levels in MSI-H CRC. Both of these articles employed single-cell RNA sequencing technology. In the future, we may collect MSI-H samples related to CRC for single-cell sequencing, allowing for a more in-depth exploration of the specific molecular mechanisms, immune microenvironment, and molecular heterogeneity in the development of MSI-H CRC, as well as its association with the response to immune checkpoint inhibitor therapy. Conclusion This study reveals the presence of an active immune response in MSI-H tumors along with reduced levels of lipid metabolism and abnormal pathway phenotypes related to the proliferation and migration of Wnt/β-catenin and the MAPK pathway. Declarations Data availability The datasets generated and/or analyzed during the current study are available in the TCGA repository and. Acknowledgements Not applicable. Funding This study was supported by the National Natural Science Foundation of China (No. 81760442). Author information Yan Xiong and Weiqiang Xiong have contributed equally to this work and share first authorship. Authors and Affiliations Department of Digestive Oncology, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, No.519 Beijing East Road, Qingshanhu District, Nanchang City, Jiangxi Province, 330029, China Yan Xiong, Weiqiang Xiong, Yanhua Wang, Chuan He, Yimei Zhan, Lili Pan, Liangping Luo, Rongfeng Song Disclosure The author(s) report no conflicts of interest in this work. Ethics declarations Ethics approval and consent to participate Not applicable. Contributions All authors contributed to this present work: [Yan Xiong] and [Weiqiang Xiong] designed the study, [Lili Pan] and [Liangping Luo] acquired the data. [Yanhua Wang] and [Chuan He] analyzed the data and edited the manuscript, [Yimei Zhan] revised the manuscript. All authors read and approved the manuscript. References Vilar, E. and S.B. Gruber, Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol, 2010. 7 (3): p. 153-62. 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Fam Cancer, 2016. 15 (3): p. 405-12. Wu, T., et al., Single-cell sequencing reveals the immune microenvironment landscape related to anti-PD-1 resistance in metastatic colorectal cancer with high microsatellite instability. BMC Med, 2023. 21 (1): p. 161. Wang, R., et al., Single-cell genomic and transcriptomic landscapes of primary and metastatic colorectal cancer tumors. Genome Med, 2022. 14 (1): p. 93. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4090131","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285806982,"identity":"3c391a83-e844-452e-9187-aa446dc35042","order_by":0,"name":"Yan Xiong","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Xiong","suffix":""},{"id":285806983,"identity":"a0cb07d2-5bf1-4f62-b556-f6d9155afa8d","order_by":1,"name":"Weiqiang Xiong","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Weiqiang","middleName":"","lastName":"Xiong","suffix":""},{"id":285806984,"identity":"7a43c553-5386-43ba-b65e-1916b3ed39f9","order_by":2,"name":"Yanhua Wang","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Wang","suffix":""},{"id":285806985,"identity":"e4cb6723-6fda-4f44-b0af-8bbc63a8c189","order_by":3,"name":"Chuan He","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"He","suffix":""},{"id":285806986,"identity":"ae552568-6019-47a1-9eee-afd1805eaf85","order_by":4,"name":"Yimei Zhan","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yimei","middleName":"","lastName":"Zhan","suffix":""},{"id":285806988,"identity":"00233a8c-fafd-4876-bca8-c7d8b1bcf28d","order_by":5,"name":"Lili Pan","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Pan","suffix":""},{"id":285806989,"identity":"5187c21e-00c7-41a3-a385-8277b944ab1b","order_by":6,"name":"Liangping Luo","email":"","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Liangping","middleName":"","lastName":"Luo","suffix":""},{"id":285806990,"identity":"0cb845b7-dc4b-484d-bcb3-d170d1fb1580","order_by":7,"name":"Rongfeng Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACxgYowwBM2tjw8PM34FKMVUtamozkjANEWgfVctjGoCEBv0rmGckPPxfU2OSbs7c//lyRcJ7HgOEA44ePOXgc1nPMWHrGsTTLnT1nzCTPJNzmMWduYJacuQ2PlvYeBmnehsMGBjdy2Bgbf9zmsWw4wMbMi09LMw/zb96G/0At6Y8/NiSc4zE4kEBAS3sPG9CWA0AtCQaSDQkHiNDSc8zMmudYsoHBGaBfGhKSeSRnHGzG6xfDGcmPb/PU2BkYHG8HOczOnp+/+eCHj/i0NGCxGYsYEpDHKzsKRsEoGAWjAAQAyRFQ0Kcb83MAAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College","correspondingAuthor":true,"prefix":"","firstName":"Rongfeng","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-03-13 07:00:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4090131/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4090131/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53976198,"identity":"d2683816-cf9c-4042-b16a-6e10b5d95b00","added_by":"auto","created_at":"2024-04-03 00:44:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":701576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferent genomic variations in tumor metabolism were investigated.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eThe oncoplot presented the mutation landscape of all CRC patients in TCGA. \u003cstrong\u003e(B)\u003c/strong\u003ePatients were categorized, with brown representing MSI-H type CRC patients, and gray representing MSI-L type CRC patients.\u003cstrong\u003e (C) \u003c/strong\u003eThe top 10 genes with the highest mutation frequencies were listed. \u003cstrong\u003e(D) \u003c/strong\u003eTumor mutation burden in CRC patients was depicted using a scatter plot. \u003cstrong\u003e(E) \u003c/strong\u003eBox plots were used to show the difference in tumor mutation burden between MSI-H and MSI-L type CRC patients. \u003cstrong\u003e(F) \u003c/strong\u003eCopy number variations in MSI (H/L) patients were presented using a bar chart.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/f94d12553e4602edd3e45bdd.png"},{"id":53976164,"identity":"1615eaf1-9bee-48f2-b014-05ec5aca76a7","added_by":"auto","created_at":"2024-04-03 00:36:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":865334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis of Differential Genes Based on MSI Features.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eVolcano plot showing the number of differential genes between MSI-H and MSI-L phenotypes. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plot showing the number of differential genes between MSI-H and MSS phenotypes. \u003cstrong\u003e(C)\u003c/strong\u003e GO analysis showing upregulated genes in MSI-H vs MSI-L involved in various immune-related signaling pathways. \u003cstrong\u003e(D)\u003c/strong\u003e GO analysis showing upregulated genes in MSI-H vs MSS involved in various immune-related signaling pathways. \u003cstrong\u003e(E)\u003c/strong\u003e GO analysis showing downregulated genes in MSI-H vs MSI-L involved in substance transport, hormone, and lipid metabolism-related signaling pathways. \u003cstrong\u003e(F)\u003c/strong\u003e GO analysis showing downregulated genes in MSI-H vs MSS involved in substance transport, hormone, and lipid metabolism-related signaling pathways. \u003cstrong\u003e(G)\u003c/strong\u003e GSEA analysis showing suppression of lipid metabolism-related pathways in MSI-H.(H) GSEA analysis showing activation of immune-related pathways in MSI-H.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/adc3ba15a4c3022499975500.png"},{"id":53976166,"identity":"add166fb-aff8-4bf0-a413-262cd37b5c3c","added_by":"auto","created_at":"2024-04-03 00:36:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":659911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of gene co-expression networks using the WGCNA algorithm.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Outlier samples removed through hierarchical clustering. \u003cstrong\u003e(B)\u003c/strong\u003eSelection of the optimal weighting coefficient to fit the scale-free network distribution. \u003cstrong\u003e(C)\u003c/strong\u003e A total of 25 modules obtained based on the clustering. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation analysis between modules. \u003cstrong\u003e(E)\u003c/strong\u003e Blue module most correlated with the MSI-H phenotype in tumor samples. \u003cstrong\u003e(F)\u003c/strong\u003e GO analysis showing the blue module's association with lipid transport. \u003cstrong\u003e(G)\u003c/strong\u003eKEGG analysis showing the blue module's association with cell proliferation signaling pathways. \u003cstrong\u003e(H)\u003c/strong\u003e Brown module most correlated with high immune cell infiltration in MSI-H samples. \u003cstrong\u003e(I)\u003c/strong\u003e GO analysis showing the brown module's association with cellular molecular metabolic processes. \u003cstrong\u003e(J)\u003c/strong\u003eKEGG analysis showing the brown module's association with cell metabolism and oxidative phosphorylation signaling.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/8d836623c6330fec6eef4e2f.png"},{"id":53976167,"identity":"60a88716-e9f4-4130-a7c3-c9a6656ca0aa","added_by":"auto","created_at":"2024-04-03 00:36:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":421205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and Independent Prognostic Analysis of Key Genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The process of compression of variables by lasso regression algorithm. The upper horizontal axis refers to the number of variables, the lower horizontal axis refers to the lambda parameter value after taking log, and the vertical axis refers to the coefficient of each feature in the function. \u003cstrong\u003e(B) \u003c/strong\u003eSelect the most appropriate penalty lambda, 1se: lambda value representing the 1 standard error with the least error. \u003cstrong\u003e(C)\u003c/strong\u003eAccuracy of the model in predicting patient survival, AUC=0.824. \u003cstrong\u003e(D-H)\u003c/strong\u003eAccording to the KM analysis of the association of characteristic gene expression with overall survival of colon cancer patients, the red group represented more than the median expression of this gene, and the blue group represented less than the median expression of this gene. The genes GDE1, MRPL22, DNAH1, FAM178A and COX7A2 were analyzed successively.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/c714b6b14aa3998c9509bd0d.png"},{"id":53976199,"identity":"95cb2260-0a4b-4fa8-98c5-23f1630144bb","added_by":"auto","created_at":"2024-04-03 00:44:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":682810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of immune cell infiltration landscape in colon cancer patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBox plots comparing 26 immune cells between MSI-H and MSI-L colon cancer patients. Significance levels are denoted as *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for not significant. \u003cstrong\u003e(B) \u003c/strong\u003eUnsupervised clustering of the MSI-H group, leading to the identification of two patient subgroups. \u003cstrong\u003e(C)\u003c/strong\u003eDendrogram illustrating the clustered samples. \u003cstrong\u003e(D)\u003c/strong\u003e Violin plots depicting the expression levels of 6 immune cells that show significant differences in distinct immune infiltration subgroups.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/63248e0bd002f566ea3ca1ff.png"},{"id":53976168,"identity":"f61629c6-b7a2-4d43-8b1b-a66fd97e1198","added_by":"auto","created_at":"2024-04-03 00:36:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":546391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic variation key genes and pathway activation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eMutation profile of low immune infiltration samples in MSI-H patients based on TCGA-COAD mutation data.\u003cstrong\u003e (B)\u003c/strong\u003e The mutation profile of high immune infiltration samples in MSI-H patients is presented.\u003cstrong\u003e (C) \u003c/strong\u003eThe top 7 genes with the highest mutation frequencies in MSI-H patients with low immune infiltration. \u003cstrong\u003e(D)\u003c/strong\u003e The top 7 genes with the highest mutation frequencies in MSI-H patients with high immune infiltration are displayed.\u003cstrong\u003e (E, F)\u003c/strong\u003eFunctional enrichment analysis is conducted to explore the biological processes and pathways associated with the differentially expressed genes.\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/e589e364353ac1daf0eb10e0.png"},{"id":64664274,"identity":"58b89f17-8c17-4f83-9476-0c03c84763e4","added_by":"auto","created_at":"2024-09-17 08:46:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4499548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4090131/v1/a7307432-0c82-443a-bcb8-dbc060f33fac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CRC genome-driven metabolic reprogramming and immune microenvironment remodeling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer is a common malignant tumor of the digestive tract, with high incidence and mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The occurrence and development of colorectal cancer are closely related to microsatellite instability (MSI)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MSI refers to the phenomenon of microsatellite sequence length changes during DNA replication due to defective DNA mismatch repair (MMR) system, which can be classified into high-frequency (MSI-H), low-frequency (MSI-L), and stable (MSS) types[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MSI testing is of significant clinical importance for the diagnosis, treatment, and prognosis of colorectal cancer, and currently, there are mainly two methods: molecular biology and immunohistochemistry[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, selecting appropriate treatment strategies based on MSI status is crucial for improving patients' quality of life and extending survival.\u003c/p\u003e \u003cp\u003eMSI-H tumors have a high mutation burden and high neoantigen load, which can induce immune cell infiltration and immune response, thus showing good sensitivity to immune checkpoint inhibitors (ICI)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In recent years, immunotherapy, especially immune checkpoint blockade (ICB), has become an innovative approach and has shown remarkable therapeutic effects in various cancer types such as melanoma, renal cancer, and lung cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, MSI-H tumors also exhibit certain heterogeneity, leading to some patients being resistant or unresponsive to ICI treatment[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The relationship between MSI-related immune cell infiltration and immune therapy is a complex process, involving various factors, such as the tumor microenvironment, intracellular signaling pathways of tumor cells, immune cell subsets, immune checkpoint molecules, tumor-associated antigens, etc[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our research, we focused on exploring the genomic and transcriptomic characteristics of different MSI samples in CRC. We discovered driver genomic events and further analyzed the transcriptomic features of MSI-H samples. The metabolic reprogramming of MSI-H was identified, and we explored the characteristics of higher immune infiltration in MSI-H patients. Moreover, through machine learning algorithms, we analyzed the driver genes in MSI-H samples with high immune infiltration and compared their molecular features. Our study provides new insights into immunotherapy targeting MSI-H in CRC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eColorectal Cancer Dataset and Data Processing\u003c/h2\u003e \u003cp\u003eRNA-Seq data of 401 COAD tumor samples, including TPM (Transcripts Per Million) data and counts data, along with corresponding clinical features, were downloaded from The Cancer Genome Atlas (TCGA) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/projects/TCGA-COAD\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/projects/TCGA-COAD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Ensembl IDs were converted to official gene symbols, and low-expressed genes were further analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCRC Genomic Variation Analysis\u003c/h2\u003e \u003cp\u003eWe downloaded CRC somatic mutation data using the R package \"maftools\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/maftools/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/maftools/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Subsequently, we plotted the mutation landscape for all CRC patients and identified differentially mutated genes between MSI-H and MSI-L. Additionally, we computed and compared the tumor mutation burden (TMB) between MSI-H and MSI-L. To visualize copy number variations in MSI(H/L) patients, we utilized bar plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Immune Cell Infiltration\u003c/h2\u003e \u003cp\u003eIn this study, the R package \"xCell\" was utilized to assess the landscape of immune cell infiltration in MSI-H and MSI-L patients. \"xCell\" is a gene signature-based method that combines gene set enrichment and deconvolution techniques to analyze microarray and RNA-seq expression profiles[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This method has the capability to predict the abundance of 64 cell types, including immune cells, hematopoietic cells, and epithelial cells. Additionally, we employed the ssGSEA method to estimate the infiltration levels of 28 immune cell subtypes in each sample, encompassing B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, NK cells, neutrophils, macrophages, and more. To further classify COAD samples into distinct subgroups, we utilized the \"pheatmap\" R package with default parameters for clustering, which stratified all COAD samples into two subtypes based on the pattern of immune cell infiltration, namely, the high immune subtype and the low immune subtype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMutation Data Processing\u003c/h2\u003e \u003cp\u003eTo validate the mutation information of different immune subtypes in MSI-H, we downloaded somatic mutation data in maf format based on the hg19 reference genome from TCGA. We utilized the \"oncoplot\" function in the R package \"Maftools\" to generate an oncogenic plot, displaying the mutation status of the top 20 driver genes with the highest alternate allele frequency in each sample. Additionally, we employed the \"mafCompare\" function to compare the differences in driver genes between different subtypes and visualized the results as frequency bar plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression Analysis\u003c/h2\u003e \u003cp\u003eThe COAD samples were divided into three groups: MSI-H, MSI-L, and MSS groups. DESeq2 was used for differential expression analysis, comparing the MSI-H group with the other two groups. The criteria for differential expression were set as |log2(fold change)| \u0026ge; 1 and False Discovery Rate (FDR)\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Key Genes Associated with MSI-H and Immune Infiltration\u003c/h2\u003e \u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) was used to identify key prognostic genes associated with the MSI-H group in colorectal cancer samples. First, an appropriate soft threshold was chosen to transform the Adjacency Matrix (AM) into the Topological Overlap Matrix. Then, the correlation between gene consensus modules and MSI status, as well as immune infiltration, was analyzed. Modules that were significantly correlated with MSI-H and immune infiltration were selected for further analysis. Key genes were screened based on a correlation greater than 0.8 between genes and modules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe 'clusterProfiler' R package was used for functional annotation of differentially expressed genes and module genes based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Functional enrichment pathways were selected based on adjusted p-values\u0026thinsp;\u0026le;\u0026thinsp;0.05, indicating significant enrichment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eThe key genes selected from the relevant modules were subjected to survival analysis of colorectal cancer patients using the GEPIA (Gene Expression Profiling Interactive Analysis) (cancer-pku.cn) web tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Model Establishment\u003c/h2\u003e \u003cp\u003eRegression analysis was performed using the \"survival\" R package to screen potential prognostic genes among the brown module genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was applied to these prognostic candidate genes. Finally, the best prognostic model containing eight genes was established by selecting the optimal penalty parameter λ associated with the minimum 10-fold cross-validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eKey genomic events associated with different MSI phenotypes in CRC\u003c/h2\u003e\n \u003cp\u003eTo explore the genetic mutation characteristics of MSI (H/L) patients in colorectal cancer (CRC), we used the R package \u0026quot;maftools\u0026quot; to download CRC somatic mutation data and generated mutation plots for all CRC patients (Fig. \u003cspan\u003e1\u003c/span\u003e-A). We found significant differences in gene mutations between MSI (H/L) patients and the general CRC population, with MSI-H patients showing higher mutation frequencies and quantities (Fig. \u003cspan\u003e1\u003c/span\u003e-B). Further comparison revealed 84 differentially mutated genes in MSI-H samples, most of which were specific to MSI-H. We listed the top 10 genes with the highest mutation frequencies (Fig. \u003cspan\u003e1\u003c/span\u003e-C). Additionally, we calculated the tumor mutation burden (TMB), and the results indicated that MSI-H samples had a higher TMB level (Fig. \u003cspan\u003e1\u003c/span\u003e-D and E). Furthermore, we analyzed copy number variations in MSI (H/L) patients, and the figure showed that PADI1, CCT6P3, SEMA3E, RNA5SP251, SLC35G5, and VPS37A tended to have more copy number variations in MSI-L samples (Fig. \u003cspan\u003e1\u003c/span\u003e-F). This further underscores the presence of distinct genomic variations in MSI (H/L) patients with CRC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eCharacterization of the transcriptional profile of MSI-H patients\u003c/h2\u003e\n \u003cp\u003eColorectal cancer patients with dMMR/MSI-H tumors are significantly more sensitive to Immune Checkpoint Inhibitors (ICIs) than those with MSS)/MSI-L tumors. To explore the key molecular pathways that drive the regulation of the immune microenvironment in MSI-H tumors, we downloaded data from 456 colon cancer samples from the TCGA database (\u003cspan\u003e\u003cspan\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003c/span\u003e). First, we grouped the samples based on the patients\u0026apos; clinical phenotypes into Microsatellite High Instability (MSI-H), Microsatellite Low Instability (MSI-L), and Microsatellite Stable (MSS) groups. Then, we performed differential gene analysis between MSI-H and MSI-L as well as MSS samples, identifying 1748 and 1904 differentially expressed genes, respectively (Fig. \u003cspan\u003e2\u003c/span\u003eA, B). We conducted GO and KEGG database enrichment analysis on the upregulated and downregulated genes among the differentially expressed genes. We found that downregulated genes in the comparison between MSI-H and MSI-L were significantly enriched in signal pathways related to substance transport, such as organic anion transport, lipid transport, and sterol transport pathways. They were also enriched in hormone and metabolism-related pathways, including hormone level regulation, steroid metabolic process, and hormone metabolic process (Fig. \u003cspan\u003e2\u003c/span\u003eB, C). On the other hand, upregulated genes were significantly enriched in signal pathways related to immune cell recruitment and activation, such as interferon-gamma response pathway, leukocyte-mediated immunity, and positive regulation of T cell activation (Fig. \u003cspan\u003e2\u003c/span\u003eC, E). Interestingly, the enrichment results of differentially expressed genes between MSI-H and MSS were almost identical to the previous results (Fig. \u003cspan\u003e2\u003c/span\u003eD,F). This suggests that MSI-H is the major contributor to tumor-independent subtyping. In conclusion, these results indicate that the immune-related pathways driven by MSI-H tumor cells simultaneously inhibit substance transport signal pathways.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eIdentification of Key MSI-H-Related Modules by WGCNA\u003c/h2\u003e\n \u003cp\u003eTo identify key genes related to the MSI-H phenotype and high immune infiltration, we first performed hierarchical clustering on the 456 colon cancer samples and removed outlier samples through branch trimming (Fig. \u003cspan\u003e3\u003c/span\u003e). Then, we used the WGCNA method to construct a weighted correlation network based on the remaining samples to identify gene modules most correlated with MSI-H and high immune infiltration. We chose 4 as the soft thresholding parameter to construct the scale-free gene co-expression network and obtained a total of 25 gene modules. Among them, we found that the blue module had the highest correlation with the MSI-H phenotype (cor = -0.66, p\u0026thinsp;\u0026le;\u0026thinsp;0.001) (Fig. \u003cspan\u003e3\u003c/span\u003eA-E).To analyze the main molecular pathways involved in the negative correlation between the blue module and MSI-H phenotype, we performed functional enrichment annotation of the genes within the module using the GO and KEGG databases (Fig. \u003cspan\u003e3\u003c/span\u003eF-G). GO annotation showed that the genes in the blue module were mainly related to substance transport, such as anion transport, organic hydroxy compound transport, and lipid transport signaling pathways. They were also associated with pathways related to cellular responses to foreign substances. KEGG annotation revealed that the blue module was related to digestion-related pathways, such as bile secretion and gastric acid secretion, as well as pathways associated with cell growth and differentiation, such as the Wnt signaling pathway, ErbB signaling pathway, and VEGF signaling pathway (Fig. \u003cspan\u003e3\u003c/span\u003eH, I).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eMolecular Features of High Immune Infiltration in MSI-H\u003c/h2\u003e\n \u003cp\u003eImmune cell infiltration is closely related to tumor metastasis. To explore the relationship between MSI and immune cell infiltration and its association with patient prognosis, we further divided the MSI-H samples into high immune infiltration and low immune infiltration phenotypes. We then performed correlation analysis between the modules and phenotypes (Fig. \u003cspan\u003e3\u003c/span\u003eH), and found that the brown module was most correlated with high immune infiltration (cor = -0.27, p\u0026thinsp;\u0026le;\u0026thinsp;0.001). We conducted functional annotation analysis on the genes within the brown module using the GO database, which showed that the module was mainly involved in cellular molecular metabolic processes, chromatin activities in the cell nucleus including DNA repair, chromatin structure organization, transcriptional regulation, and protein localization (Fig. \u003cspan\u003e3\u003c/span\u003eI). We identified key genes within the module using the threshold screening method (module membership\u0026thinsp;\u0026ge;\u0026thinsp;0.8) and found 15 genes that met the criteria in the brown module. Subsequently, we used the LASSO model to construct a prognosis risk assessment model based on only 8 genes. The cvfit and lambda curve are shown in Fig. \u003cspan\u003e4\u003c/span\u003eA, B. We then plotted the survival curves of colon cancer patients with significant genes in the module and genes used to establish the prognosis risk assessment model using the online tool GEPIA. Five genes were found to be significantly associated with patient survival: COX72A, GED1, and MRPL22 were identified as protective genes associated with prolonged survival, while DNAH1 and FAM178A were identified as risk genes associated with shortened survival (Fig. \u003cspan\u003e4\u003c/span\u003eD-H).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eMSI-H tumors show increased infiltration of immune cells\u003c/h2\u003e\n \u003cp\u003eTo observe the changes in the tumor microenvironment in colorectal cancer (CRC), we employed the xCell algorithm to determine the landscape of immune cell infiltration in all COAD patients from the TCGA database (Fig. \u003cspan\u003e5\u003c/span\u003eA). It was observed that the MSI-H group exhibited more pronounced immune infiltration. Furthermore, we performed an in-depth analysis of specific immune cells in the MSI-H group. Using the ssGSEA algorithm, we downloaded marker genes for 28 immune cell types from the internet and assessed the immune infiltration levels in 79 MSI-H samples derived from TCGA-COAD. By clustering MSI-H patients using the R package \u0026quot;pheatmap,\u0026quot; two distinct infiltration patterns were identified: high immune infiltration and low immune infiltration (Fig. \u003cspan\u003e5\u003c/span\u003eB), and the clustered samples were visualized in Fig. \u003cspan\u003e5\u003c/span\u003eC. Upon examining the differences in immune cell infiltration levels between high and low immune infiltration groups, significant variations were found in activated B cells, activated CD4 T cells, and activated CD8 T cells (Fig. \u003cspan\u003e5\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eMolecular features of elevated immune cell infiltration in MSI-H\u003c/h2\u003e\n \u003cp\u003eTo determine the differences in cancer-related gene mutations between the high immune infiltration and low immune infiltration groups, we first calculated the gene mutations in each group. Representative gene mutations in both groups are shown in Fig. \u003cspan\u003e6\u003c/span\u003eA-B. For MSI-H patients, examining the mutation patterns in low immune infiltration samples (Fig. \u003cspan\u003e6\u003c/span\u003eA) revealed that TTN (90%), MUC16 (74%), RNF43 (74%), BMPR2 (62%), and BRAF (62%) were the top five genes with the highest mutation frequencies. On the other hand, KMT2D (73%), RNF43 (73%), SYNE1 (73%), FAT4 (69%), and other genes exhibited higher mutation frequencies in MSI-H patients with high immune infiltration (Fig. \u003cspan\u003e6\u003c/span\u003eB). We compared the distribution of COAD gene mutations in the low and high immune infiltration subgroups, as shown in Fig. \u003cspan\u003e6\u003c/span\u003eC-D. We identified a total of 176 differentially mutated genes, with 63 genes showing a higher mutation frequency in the Low group, while the remaining 113 genes exhibited a higher mutation frequency in the High group. Functional enrichment analysis of the differentially expressed genes (Fig. \u003cspan\u003e6\u003c/span\u003eE-F) revealed significant enrichment in cell adhesion, antigen presentation, protein binding, as well as the regulation of channel activity and synaptic transmission pathways.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe frequency of dMMR/MSI-H tumors in CRC is approximately 15%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MSI refers to the phenomenon of microsatellite length changes caused by insertion or deletion mutations during DNA replication. MSI can be classified into MSI-H, MSI-L, and MSS based on the degree of instability, and MSI is a result of MMR protein dysfunction[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Tumors with MSI-H or dMMR features are generally more responsive to immune checkpoint therapy[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Pembrolizumab, a humanized IgG4 antibody, was the first immune checkpoint-targeted drug that showed promising results in patients with dMMR colorectal cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nivolumab, another PD-1 humanized monoclonal antibody, was approved by the FDA for the treatment of dMMR or MSI-H metastatic colorectal cancer in 2017[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. MSI status can alter the tumor microenvironment (TME) of CRC patients in multiple ways, thus affecting the efficacy of ICIs in CRC patients. MSI-H CRC has more immune cell infiltration, which is associated with better prognosis. However, there is currently no systematic explanation of the differences in immune microenvironment among MSI subtypes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the differential analysis between MSI-H and MSS/MSI-L, we found that signal pathways related to lipid and hormone metabolism were significantly inhibited, including cholesterol homeostasis and hormone metabolic processes. Immune-related pathways were significantly activated, including leukocyte migration involved in inflammatory response and positive regulation of T cell chemotaxis. Some studies have shown that statin drugs can reduce the incidence of colon cancer[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, another study suggested that the high cholesterol levels induced by statins can lower the risk of colorectal cancer. Additionally, they observed that a decrease in serum cholesterol levels within at least one year before cancer diagnosis is associated with an increased risk of colorectal cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Obesity is also a potential factor that induces colon cancer, and obesity-related hormone metabolic disorders can cause macrophage polarization and cytokine expression, directly affecting tumor progression and survival in CRC patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, it was also observed that the brown module in MSI-H is negatively correlated with high immune cell infiltration and contains key genes such as COX7A2, MRPL22, and KMT2C. COX7A2 is a subunit of cytochrome c oxidase (COX), which is a complex in the mitochondrial respiratory chain. While there is limited research on the relationship between COX7A2 and colon cancer, expression of COX7A1 is significantly downregulated in lung cancer patients, and it is closely associated with non-small cell lung cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is possible that COX7A2 may have a similar connection in colon cancer. MRPL22 encodes mitochondrial ribosomal protein L22, which is involved in the composition of mitochondrial ribosomes and participates in mitochondrial protein synthesis. Currently, research on MRPL22 in colon cancer is relatively limited. There is a report suggesting that LARP1-MRPL2 is a novel and recurrent fusion gene in non-Hodgkin B-cell lymphoma[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. KMT2C encodes a protein called MLL3, which is a member of the histone methyltransferase (HMT) family responsible for adding methyl groups to the genome. KMT2C mutations are frequently observed in various cancers. that KMT2C inactivation may promote colorectal cancer development through transcriptional dysregulation[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis revealed the genomic characteristics of MSI (H/L) patients, with MSI-H patients exhibiting higher mutation frequencies. Previous research has indicated that MSI-H/dMMR (Microsatellite Instability-High/Deficient Mismatch Repair) and TMB-H (High Tumor Mutation Burden) reflect the high genomic mutation frequency, which may suggest the efficacy of immune checkpoint inhibitor therapy[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, studies have shown that some CRC patients develop resistance to PD-1 inhibitors after treatment, with a subset presenting novel MMR defects or new microsatellite instability (nMMRD/nMSI). These novel defects lead to a decrease in tumor mutation burden (TMB) and neoantigen burden (TNB), thereby reducing tumor sensitivity to immune checkpoint inhibitors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our analysis, we identified FAT4, BRAF, APC, and TTN as genes associated with MSI-H, exhibiting higher mutation frequencies and numbers in MSI-H patients, thus influencing tumor initiation, progression, and treatment response[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These genes participate in different signaling pathways, such as the Wnt/β-catenin pathway, MAPK pathway, PI3K/AKT pathway, among others[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Overall, continuous research on the genomic characteristics of MSI (H/L) patients will lead to further discoveries and advancements in the understanding and treatment of colorectal cancer[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis also revealed differences in immune cell infiltration in MSI-H tumors. While previous studies have shown no significant difference in overall survival (OS) between MSI-H and MSS (microsatellite stable) patients[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], there are results suggesting a higher number of M0 macrophages and fewer plasma cells, as well as reduced CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, in tumors at later stages[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, our findings indicate that MSI-H CRC patients exhibit varying degrees of infiltration for B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells, suggesting changes in the tumor microenvironment for MSI-H patients. In our final analysis of the molecular characteristics of high immune cell infiltration in MSI-H tumors, we discovered that MSI-H CRC patients also exhibit certain mechanisms of immune evasion and resistance to treatment, such as T cell tolerance, T cell exclusion, and T cell exhaustion. These mechanisms may be associated with the enrichment of specific pathways in MSI-H CRC, including cell adhesion, antigen presentation, protein binding, as well as the regulation of channel activity and synaptic transmission. These findings are consistent with previous research and suggest potential targets for therapeutic interventions in MSI-H CRC patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough we have already discussed some genes related to MSI and CRC, many genes have not been reported yet. More in vitro and in vivo experiments will be required in the future to validate the roles of these genes in CRC development. Additionally, there might be other omics alterations. For example, in a study by Wu Tao et al[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. They conducted single-cell RNA sequencing on 23 MSI-H mCRC patients and found a significant decrease in the proportion of CD8\u0026thinsp;+\u0026thinsp;T cells and a significant increase in M2 macrophages in the treatment-resistant group compared to the treatment-sensitive group. Furthermore, the team led by Fuchou Tang performed single-cell RNA sequencing[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], whole-genome sequencing, and multi-region whole-exome sequencing on 12 mCRC patients, discovering heterogeneity at the genomic, epigenetic, and transcriptomic levels in MSI-H CRC. Both of these articles employed single-cell RNA sequencing technology. In the future, we may collect MSI-H samples related to CRC for single-cell sequencing, allowing for a more in-depth exploration of the specific molecular mechanisms, immune microenvironment, and molecular heterogeneity in the development of MSI-H CRC, as well as its association with the response to immune checkpoint inhibitor therapy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals the presence of an active immune response in MSI-H tumors along with reduced levels of lipid metabolism and abnormal pathway phenotypes related to the proliferation and migration of Wnt/β-catenin and the MAPK pathway.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the\u0026nbsp;TCGA repository and.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 81760442).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan Xiong and Weiqiang Xiong have contributed equally to this work and share first authorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Digestive Oncology, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, No.519 Beijing East Road, Qingshanhu District, Nanchang City, Jiangxi Province, 330029, China\u003c/p\u003e\n\u003cp\u003eYan Xiong,\u0026nbsp;Weiqiang Xiong,\u0026nbsp;Yanhua Wang, Chuan He,\u0026nbsp;Yimei Zhan,\u0026nbsp;Lili Pan,\u0026nbsp;Liangping Luo,\u0026nbsp;Rongfeng Song\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) report no conflicts of interest in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to this present work: [Yan Xiong] and [Weiqiang Xiong] designed the study, [Lili Pan] and [Liangping Luo] acquired the data. [Yanhua Wang] and [Chuan He] analyzed the data and edited the manuscript, [Yimei Zhan] revised the manuscript. All authors read and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVilar, E. and S.B. Gruber, \u003cem\u003eMicrosatellite instability in colorectal cancer-the stable evidence.\u003c/em\u003e Nat Rev Clin Oncol, 2010. \u003cstrong\u003e7\u003c/strong\u003e(3): p. 153-62.\u003c/li\u003e\n\u003cli\u003eDe\u0026apos; Angelis, G.L., et al., \u003cem\u003eMicrosatellite instability in colorectal cancer.\u003c/em\u003e Acta Biomed, 2018. \u003cstrong\u003e89\u003c/strong\u003e(9-S): p. 97-101.\u003c/li\u003e\n\u003cli\u003eZaanan, A., et al., \u003cem\u003eMicrosatellite instability in colorectal cancer: from molecular oncogenic mechanisms to clinical implications.\u003c/em\u003e Cell Oncol (Dordr), 2011. \u003cstrong\u003e34\u003c/strong\u003e(3): p. 155-76.\u003c/li\u003e\n\u003cli\u003eKawakami, H., A. Zaanan, and F.A. 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MSI stems from DNA mismatch repair issues, categorized as MSI-High (MSI-H), MSI-Low (MSI-L), or Stable (MSS). Tailoring treatments based on MSI status is vital. MSI-H tumors, with high mutation and neoantigen loads, respond well to immune checkpoint inhibitors (ICIs). However, some MSI-H tumors display resistance due to complex factors like the tumor microenvironment, signaling pathways, immune cells, and checkpoint molecules.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThrough the analysis of CRC genomic data, we identified the key genomic events that drive MSI. At the same time, through transcriptome analysis, we discovered the key genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe performed a differential analysis between MSI-H and MSS/MSI-L and found that signaling pathways involved in lipid and hormone metabolism were significantly inhibited, including cholesterol homeostasis and hormone metabolism processes. At the same time, immune-related pathways were significantly activated. We identified genes associated with MSI-H, such as FAT4, BRAF, APC, and TTN, that were mutated at a higher frequency and number in MSI-H patients, thereby affecting tumor initiation, progression, and treatment response. These genes participate in different signaling pathways, such as Wnt/β-catenin pathway, MAPK pathway, PI3K/AKT pathway, etc.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals the presence of an active immune response in MSI-H tumors along with reduced levels of lipid metabolism and abnormal pathway phenotypes related to the proliferation and migration of Wnt/β-catenin and the MAPK pathway.\u003c/p\u003e","manuscriptTitle":"CRC genome-driven metabolic reprogramming and immune microenvironment remodeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 00:36:10","doi":"10.21203/rs.3.rs-4090131/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":"8d7ce6bc-c38d-4cab-b689-e60402cd8f7d","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-17T08:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 00:36:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4090131","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4090131","identity":"rs-4090131","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}