T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer

T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer Yilin Wang, Peizhu Su, Qinghua Lu, Huiwen Huang, Zhaotao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4933597/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Treatment options for colorectal cancer are limited. T cell exhaustion is one of the barriers to tumor immunotherapy. No comprehensive analysis of T cell exhaustion-related exosome prognostic models for colorectal cancer (CRC) has been conducted. Method Samples were collected from the Cancer Genome Atlas (TCGA) database, exoRBase database and Gene Expression Omnibus (GEO) database. The single sample gene set enrichment analysis (ssGSEA) algorithm screened out T cell exhaustion-related exosome differential expression genes, signature genes were screened by univariate Cox regression and Lasso regression, and risk score models were constructed and validated. A nomogram containing risk scores and clinical parameters was established and evaluated. In addition, single cell analysis and tumor immune microenvironment assessment were also performed. Results Sixteen signature genes were identified, based on which the risk score model was constructed and validated. This model can predict the overall survival (OS) of TCGA and GEO queues well. Scores were identified as independent risk factors for OS and correlated with certain clinicopathological features. A nomogram was developed that integrated clinical parameters and risk scores and showed higher predictive accuracy. Finally, significant differences in immune microenvironment were found between the high- and low-risk groups. Thus, scores can also be used to predict the response to immunotherapy. Conclusions In general, we screened out T cell exhaustion-related exosome genes of CRC, constructed a risk score model which could predict survival and immunotherapy efficacy, and found correlations between risk scores and clinicopathologic features and immune microenvironment. T cell exhaustion exosome colorectal cancer tumor immune microenvironment bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Colorectal cancer (CRC) has the third highest incidence and mortality in the world nowadays, and is characterized by high heterogeneity and invasiveness, posing a serious threat to human health [ 1 ]. New prevention and treatment strategies are urgently needed. Clinical decisions on chemotherapy for CRC depend primarily on clinicopathological staging, regardless of molecular biological characteristics, and this inadequate decision-making approach may lead to potential over- or under-treatment. In the era of individualized therapy, it is imperative to identify reliable biomarkers to optimize the prognosis of colorectal cancer pharmacotherapy. Exosomes are tiny goblet vesicles secreted by cells. A growing number of studies have clarified the correlation between exosome production and tumorigenesis [ 2 – 4 ]. Tumor-derived exosomes participate in the exchange of genetic information between tumor cells and basal cells, affecting angiogenesis and promoting tumor growth, metastasis and invasion [ 5 ]. Numerous pieces of evidence strongly indicate that exosomes play a pivotal regulatory role in the Tumor Microenvironment (TME) of CRC, influencing factors such as flora, hypoxia, inflammation, and the immunological microenvironment [ 6 ]. As tumor markers for diagnosis and treatment, exosomes have become one of the focuses of current research. Exosome circSATB2 is associated with non-small cell lung cancer progression and therefore can potentially be used as a diagnostic marker for this cancer type [ 7 ]. Exosome circ0048117 regulates esophageal squamous cell carcinoma progression, and higher serum exosome circ0048117 is significantly and positively correlated with TNM stage [ 8 ]. Furthermore, circulating exosome miR-203 levels correlate with metastasis, and low miR-203 expression in tumor tissues is a poor prognostic factor in colorectal cancer [ 9 ]. In chronic infections or cancer, T cells gradually lose effector function and memory T cells begin to be depleted due to prolonged exposure to antigens and inflammation, this process is known as T Cell exhaustion [ 10 ], which is one of the major obstacles to anti-cancer immunotherapy. T cell exhaustion represents a unique state of T cell differentiation [ 11 ], has considerable clinical relevance, but our understanding of it is still incomplete. To date, no comprehensive analysis of T cell exhaustion-related exosome prognostic models for colorectal cancer has been conducted. The biological process of T cell exhaustion-related exosomes in colorectal cancer and the relationship between them and clinical treatment and immunotherapy of patients are still unclear. Therefore, we systematically screened out the signature genes of T cell exhaustion-related exosomes in colorectal cancer based on the existing database data, and analyzed the correlation between the signature genes and clinical treatment and immunotherapy of patients. 2. Materials and Methods 2.1 Data acquisition and preprocessing Clinical information and expression data for colonic adenocarcinoma (COAD), rectum adenocarcinoma (READ) were obtained from The Cancer Genome Atlas of America database (TCGA, https://cancergenome.nih.gov/ ), and were combined as a training set after removing the batch effects. Colorectal cancer exosome and normal exosome expression profiles were downloaded from the exoRBase database ( http://www.exorbase.org/ ). The set GSE41258 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41258 ) was downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) as a validation set, and the set GSE132465 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465 ) as a single cell quality control data set. T cell exhaustion related genes were extracted from the study of Zhang et al [ 12 ] and shown in Supplementary table 1 . 2.2 Screening of differentially expressed genes (DEGs) DEGs were filtered through the “ limma ” [ 13 ] package. The DEGs threshold point was an adjusted p value, corrected by the BH method, less than 0.05 and a |log 2 fold change| greater than 0.585. 2.3 Single sample gene set enrichment analysis (ssGSEA) Enrichment analysis for the expression profile of TCGA-COADREAD tumor samples was performed by R package “GSVA” [ 14 ] based on 40 T cell exhaustion-related genes obtained. 2.4 Screening of TEX-exo genes The intersection of the two groups of DEGs was named “exo-genes”. Spearman correlation between exo-genes expression and T cell exhaustion-related enrichment score was calculated in the training set, and the significantly related genes were retained as TEX-exo genes. 2.5 Functional enrichment analysis Gene ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses and top10 visual display of the TEX-exo genes were performed using the R package “clusterProfiler” [ 15 ], the parameters were pvalueCutoff = 0.05, pAdjustMethod = “BH”, and qvalueCutoff = 0.5. 2.6 Construction of the Signature model The hazard ratio (HR) and prognostic significance of TEX-exo genes were determined by univariate Cox regression analysis. The genes with p < 0.05 were prognostic related genes. The LASSO algorithm was implemented through the R package “glmnet” [ 16 ] for further selection and contraction of prognostic related genes. The selected genes were the signature genes. Then calculated the risk scores: Score = \(\:{\sum\:}_{i=0}^{n}\beta\:i\text{*}\chi\:i\) n for the number of signature genes, 𝛽 \(\:i\) for the weight coefficient of each gene, 𝜒 \(\:i\) for the expression of each gene. Patients were divided into high - and low-risk groups based on the median Score, the “survminer” package was used to analyze the OS and the "timeROC" package was used to plot a time-dependent receiver operator characteristic (ROC) curve. Finally, univariate and multivariate Cox analyses were performed to explore the independent prognostic value of risk Scores and to validate them in the validation set. 2.7 Nomogram construction A nomogram including clinical features and risk Scores was constructed using the R package “rms”, and a calibration curve was then drawn to assess the accuracy of the nomogram in predicting survival rate. 2.8 Single-cell analysis R package "Seurat" [ 17 ] was used for quality control of colorectal cancer samples to exclude low-quality cells and low-expression genes, and the Score of each cell was calculated according to the formula above. 2.9 Evaluation of tumor immune microenvironment (TIM) CIBERSORTx algorithm in R package “IOBR” [ 18 ] was used to calculate the Scores of 22 immune cells in tumor immune microenvironment in the training set. 2.10 Statistical analysis All analyses in this paper were carried out by R version 4.1.2. The Wilcoxon rank sum test was used to analyze the significance of two groups of numerical variables, and Kruskal − Wallis test for more than two groups. In the plots display, ns means p > 0.05, * means p < 0.05, ** means p < 0.01, *** means p < 0.001, **** means p < 0.0001. Survival curves were generated by Kaplan-Meier method, and the significance of the differences was determined by log-rank test. 3. Results 3.1 Screening of TEX-exo genes The DEGs between normal exosome and colorectal cancer exosome samples was calculated by the “limma” algorithm, and 1016 DEGs were obtained (Fig. 1 a). The expression difference of normal and tumor samples in TCGA-COADREAD was calculated by the same method, and 4352 DEGs were obtained (Fig. 1 b). Taking the intersection of the DEGs of the above two resulted in 276 differential exo-genes. The T cell exhaustion-related enrichment scores for each sample in TCGA-COADREAD are generated based on 40 T cell exhaustion-related genes with the "GSVA" algorithm, as detailed in Supplementary Table 2. Spearman correlation was calculated between the expression of 276 exo-genes and T cell exhaust-related enrichment score in TCGA-COADREAD, and 179 genes with significant correlation (p < 0.05) were retained as the TEX-exo genes for further analysis. Figure 1 c shows 8 genes with the highest correlation among the genes with significant positive correlation, including DOCK2, LSP1, HCLS1, NCKAP1L, GIMAP1, ARHGEF6, GYPC and ARHGAP25. 3.2 Functional enrichment analysis To understand the biological mechanisms related to T cell exhaustion in exosomes, KEGG and GO enrichment of TEX-exo genes were analyzed, and the results are shown in Fig. 2 a. The biological process of GO enrichment showed that these TEX-exo genes were mainly distributed in the lymphocyte proliferation, mononuclear cell proliferation, leukocyte proliferation, and so forth. The celluar component of GO enrichment suggested that these TEX-exo genes were involved in the cell cortex, secrebory granule lumen, spliceosomal snRhP complex and so on. The molecular function of GO enrichment indicated that these TEX-exo genes enriched in the carbohydrate binding, MHC protein complex binding and other functions. In addition, the KEGG results demonstrated that these TEX-exo genes enriched in Hematopoietic cell lineage, Intestinal immune network for IgA production and other pathways. To further confirm the function of proteins encoded by TEX-exo genes, we download COAD and READ level 4 protein expression data from the TCGA database ( https://www.tcpaportal.org/tcpa/ ). Among the 179 TEX-exo genes, only the MS4A1 gene was able to correspond to its matching encoded protein, CD20. Therefore, We further compared the differences of CD20 in six different clinical feature groups (age, sex, stage, colon polyps, lymphatic invasion and venous invasion) of COAD and READ. There were no significant differences in all six groups. The results can be seen in Supplementary Fig. 1. To gain further insight into the genetic variation of the TEX-exo genes, we also analyzed the incidence of copy number variation (CNV) and somatic mutations in the 179 TEX-exo genes. First of all, Fig. 2 b showed the genes with single nucleotide variation (SNV) frequency above 3%. Among them, DOCK2 exhibited the highest mutation frequency, followed by AKAP12. Then, investigation of the frequency of CNV revealed that CNV was widespread in the 179 TEX-exo genes, for example, there were extensive amplification in IGF2 and MMP9, and extensive deletion in TCEA3 and STMN1, as shown in Fig. 2 c. This systematically revealed genetic variation in TEX-exo genes, illustrating the potential pathogenic landscape of these genes in CRC. 3.3 Building the signature model Univariate Cox regression analysis was used to evaluate the effect of TEX-exo genes on OS. Nineteen genes were found to be significant prognostic factors (p 1; RBPMS2, AKAP12, SCARA3, TIMP1, HSPA1A, RBMX2 and C1orf35) were risk factors for OS and 12 (HR < 1; PPP1R16B, MS4A1, POU2AF1, BIRC3, FKBP5, DNASE1L3, CTNND1, FAM177B, SULT1B1, CEP70, UQCRFS1 and PSRC1) were protective factors for OS. Survival analysis was performed on these 19 prognostic factors in tumor samples, and the median expression level was used as the cut-off value to divide the high and low expression groups to compare the influence of expression level on prognosis. Figure 3 b showed the survival curves of 8 genes with the most significant p value (p < 0.05). There are 4 genes with low expression that have a better prognosis (HR 1; UQCRFS1, MS4A1, FAM177B and CEP70). Given the prognostic value of the 19 genes, we would like to build a more reliable and useful predictive model from these genes to guide clinical practice in colorectal cancer patients. The expression profiles of the 19 genes above were analyzed by LASSO-Cox regression analysis, and the prognostic model was constructed. 16 signature genes were identified based on the optimal value of λ 0.0087, as shown in Fig. 3 c-e. Furthermore, the regression coefficients of each signature gene were shown in Table 1. The results of univariate COX analysis of these 16 signature genes in OS are displayed in the forest plot of Fig. 3 f, and all of their expressions were strongly associated with prognosis. Table.1 Regression coefficients for 16 signature gene. Symbol Coef C1orf35 0.619111702 TIMP1 0.281871688 CTNND1 0.215980486 HSPA1A 0.170078448 RBMX2 0.1222027 CEP70 0.088315003 MS4A1 0.03913604 POU2AF1 0.032869455 RBPMS2 0.004424399 AKAP12 -0.010104709 DNASE1L3 -0.0677533 SULT1B1 -0.08324181 FAM177B -0.089640959 UQCRFS1 -0.151520918 FKBP5 -0.206617298 PSRC1 -0.401296281 Based on the coefficients calculated for each gene by the LASSO-COX algorithm and the expression values of the 16 signature genes for each CRC patient, we were able to compute the Score for each patient according to the formula in the Methods section. Patients were divided into two groups with high Score and low Score according to the median value (Fig. 4 a). Survival times for each high Score patient and low Score patient were displayed in Fig. 4 b. Overall, patients with high Score had the shorter survival time and those with low Score had the longer survival time. Moreover, the expression of the 16 signature genes in patients with high and low Score was exhibited in Fig. 4 c. Kaplan-Meier curve showed that the survival probability of patients with higher Scores was significantly lower than that of patients with lower Scores (HR = 2.97; 95% CI = 1.98–4.47; P < 0.001; Fig. 4 d). The area under ROC curve (AUC) was 0.712 in year 1, 0.676 in year 3, and 0.721 in year 5, which means that this risk scoring model has a high value for prognostic guidance (Fig. 4 e). In order to verify the stability of the model, the same algorithm was used to analyze the verification set (GSE41258). Similarly, the validation set patients were categorized into high and low Score based on the median Score (Fig. 4 f), whereas the survival time of the high Score patients remained longer overall, while the survival time of the low Score patients continued to be shorter on the whole (Fig. 4 g). In addition, the expression of 16 signature genes in high and low Score patients was demonstrated in Fig. 4 h. Consistent with results obtained in the training set TCGA-COADREAD cohort, patients in the higher Score group had a shorter survival time compared to those in the lower Score group (HR = 1.75; 95% CI = 1.18–2.61; P = 0.005; Fig. 4 i). In addition, in terms of survival effect prediction, the 1-year AUC was 0.724, 3-year AUC was 0.640, and 5-year AUC was 0.603, which reaffirmed the excellent efficacy of this risk score model in predicting prognosis (Fig. 4 j). In order to exclude the influence of other factors, univariate and multivariate Cox analyses were used to determine whether Score was an independent prognostic factor for OS. In univariate Cox analysis, Score obtained by the training set TCGA-COADREAD cohort was significantly correlated with OS (High vs. Low; HR = 2.97, 95% CI = 1.98–4.47, P < 0.001; Fig. 5 a). After adjusting for other confounding factors, multivariate Cox analysis showed that Score was still an independent predictor of OS (High vs. Low; HR = 3.47, 95% CI = 1.74–6.93, P < 0.001), as shown in Fig. 5 b. Similarly, univariate and multivariate Cox analysis was used in the verification set. In univariate Cox analysis, Score obtained by the validation set cohort was significantly correlated with OS (High vs. Low; HR = 1.75, 95% CI = 1.18–2.61, P = 0.006; Fig. 5 c). After adjusting for other confounding factors, multivariate Cox analysis showed that Score was still an independent predictor of OS (High vs. Low; HR = 1.52, 95% CI = 1.01–2.29, P = 0.043), as shown in Fig. 5 d. 3.4 Nomogram construction We combined Score with five different clinical features (age, sex, stage, lymphatic invasion and venous invasion) to construct a nomogram to predict the probability of 1-year, 3-year, and 5-year OS. Each factor was assigned in proportion to its risk contribution to survival (Fig. 6 a), and calibration curves showed that the combined model (nomogram) showed high accuracy over 1 -, 3 -, and 5-year OS (Fig. 6 b). Compared with a single prognostic factor, the nomogram constructed using a combination model may be a better predictor of patients' OS (Fig. 6 c). We analyzed the robustness of Score in different clinical features, and the results were shown in Fig. 6 d. In most clinical groups, we observed that the prognosis of the patients with low Scores was better than that of the patients with high Scores (HR < 1, P < 0.05). We also compared the differences in Scores among six different clinical feature groups, as shown in Fig. 6 e. Scores were higher in stage III/IV patients than in stage I/II patients, in patients with lymphatic invasion than in patients without lymphatic invasion, and in patients with venous invasion than in patients without venous invasion. Additionally, there were no significant differences in Scores among different age, sex and colon polyps. 3.5 Single-cell analysis The role and value of the Score was further validated using the single-cell analysis of colorectal cancer. The single-cell analysis of 23 colorectal cancer tumor samples showed that 40,000 cells were retained from the original 47,285 cells after quality control. Figure 7 a and Fig. 7 b showed the landscape of cell distribution before batch effect and after debatching effect, respectively. The distribution of the six cell types (B cells, Epithelial cells, Mast cells, Myeloids, Stromal cells and T cells) can be derived from the gene expression values of each cell (Fig. 7 c). The same formula was used to calculate the Score of each cell, and the results were displayed by using UMAP, as shown in Fig. 7 d. Among the six cell types, Stromal cells had the highest Scores, while T cells had the lowest (Fig. 7 e). The differences in Scores between the other five cells and the Epithelial cells were shown in Fig. 7 f. Among them, compared with Epithelial cells, the Scores of Mast cells, Myeloids and Stromal cells were relatively high (log2FC > 0), while the Scores of B cells and T cells were relatively low (log2FC < 0). 3.6 Evaluation of TME We further explored the tumor immune microenvironment between the high and low Scores groups with the aim of identifying the underlying immune mechanisms. Different immune cell subsets were quantified using CIBERSORTx, and rank sum tests were used to compare the significance of infiltration degree between groups. We found that the infiltration degree of B cells memory, Dendritic cells activated Macrophages M0 cells, Plasma cells, T cells CD4 memory resting, and T cells regulatory were significantly different in higer and lower Score groups. The results are shown in Fig. 8 a. We also investigated the differences in the expression of immune checkpoints in the higher and lower Score groups. Expression of all active, inhibit and two-side immune checkpoint genes differed significantly between high and low Scores (Fig. 8 b and Supplementary Fig. 2). We also explored whether prognostic signature could predict a patient's response to immune checkpoint blocking therapy. Immunotherapy data for a metastatic melanoma was used [ 19 ], it was found that patients with high Scores have a better prognosis than those with low Scores (P = 0.038; Fig. 8 c). Furthermore, patients who responded to immunotherapy drugs had higher Scores than those who did not respond to the drugs (P = 0.026; Fig. 8 d). Among the high Scores patients, 28.57% responded to immunotherapy, while 71.43% did not. In addition, all patients in the low Scores patients did not respond to immunotherapy (Fig. 8 e). Finally, the high Scores group had higher TIDE scores than the low Scores group in TCGA-COADREAD (P = 3.9e-11; Fig. 8 f). In conclusion, the immunotherapy effect of the high Scores group was better than that of the low Scores group. Therefore, Scores can be used as a novel marker to guide immunotherapy in colorectal cancer patients. 4. Discussion T cell exhaustion is critical in tumor immunotherapy [ 20 ]. Studies show that severe exhaustion of tumor-infiltrating T cells in microsatellite stabilized (MSS) colorectal cancer is one of the important mechanisms by which patients develop resistance to PD-1 inhibitors [ 21 ]. Tumor-derived exosomes (TDEs) are involved in various processes of cancer formation and development, including tumor microenvironment remodeling, angiogenesis, invasion, metastasis, and drug resistance [ 22 ]. It has been found that TDEs can promote the occurrence of liver metastasis in CRC by regulating the crosstalk between tumor cells and macrophages [ 23 ]. To our knowledge, this study is the first to systematically evaluate T cell exhaustion-related exosome genes in CRC. Firstly, a risk score model containing 16 signature genes was established through the TCGA cohort, and a nomogram was constructed to predict the OS of CRC patients, while the model was validated using an external data set. Secondly, the GSE132465 data set was used to characterize the features of the scores of signature genes in single cells. Finally, we also found that signature gene scores were associated with other clinicopathologic features and tumor immune microenvironment in patients with colorectal cancer. Previous studies have shown that non-coding RNA (ncRNA) and circular RNA (circRNA) derived from exosome can affect the tumor immune microenvironment, including inducing T cell exhaustion and promoting the overexpression of programmed death ligand-1 (PD-L1) [ 24 – 26 ]. However, considering that the cross-talk between mRNA in exosomes and T cell exhaustion on CRC needs to be explored, we identified 179 TEX-exo genes, which are not only DEGs of CRC exosomes versus normal exosomes, but also DEGs closely related to T cell exhaustion. Eight exosomal genes, including DOCK2, LSP1, HCLS1, NCKAP1L, GIMAP1, ARHGEF6, GYPC and ARHGAP25, were significantly positively correlated with T cell exhaustion (R > 0.8, p < 0.05). It has been reported that cholesterol sulfate synthesized by SULT2B1 in hepatocellular carcinoma inhibit DOCK2 activity in T cells and promote effector T cell exhaustion [ 27 ]. In addition, a combination of RNA-seq and proteomics analysis of human NCKAP1L deficiency cases reported by Castro et al. [ 28 ] showed characteristics of T cell exhaustion. 179 TEX-exo genes were mainly concentrated in immune-related molecules or signaling pathways, such as MHC class II protein complex, lymphocyte proliferation and Intestinal immune network for IgA production. Kilian et al. [ 29 ] showed that MHC Class II restricted antigen presentation is required to prevent cytotoxic T cell dysfunction in brain tumors. In addition, despite the prevalence of CNV in 179 TEX-exo genes, the relationship between T cell exhaustion and CNV remains unclear, further studies are needed. We constructed a signature gene scoring model based on 16 T cell exhaustion-related exosome genes. Signature genes scores in both the TCGA cohort and GSE41258 showed excellent prognostic ability and were identified as an independent risk factors for OS (P < 0.05). Compared with a single risk score model, a nomogram with signature genes scores and clinicopathological features is able to predict patients' OS more accurately. In addition, the current signature genes risk score is associated with some clinicopathologic features, including TNM staging, lymph node invasion, and vascular invasion. Among the 16 signature genes, RBPMS2, AKAP12, TIPM1, HSPA1A, RBMX2 and C1orf35 were negatively correlated with CRC patients' OS (HR > 1), while MS4A1, POU2AF1, FKBP5, DNASE1L3, CTNND1, FAM177B, SULT1B1, CEP70, UQCRFS1 and PSRC1 were positively correlated with OS in patients with colorectal cancer (HR < 1). He et al. [ 30 ] reported that down-regulation of AKAP12 can inhibit the progression and migration of CRC through the PI3K/AKT signaling pathway. It was reported that TIMP1 expression was significantly associated with regional lymph nodes and distant metastases [ 31 ]. The down-regulation of HSPA1A inhibits the proliferation and migration of CRC cells, and CRC patients with lower expression levels tend to have longer OS [ 32 , 33 ]. MS4A1, DNASE1L3 and SULT1B1 were significantly down-regulated in CRC tissues, and their reducing levels were associated with shorter OS in CRC patients [ 34 – 36 ]. UQCRB plays a key role in mitochondrial complex III stability, electron transport, cellular oxygen sensing and angiogenesis, and can be used as an important diagnostic marker for CRC [ 37 ]. Although our study showed a positive correlation between CTDNN1 and OS, some basic medicine studies suggested that the activation of CTDNN1 could induce the proliferation and migration of CRC cells, so the experimental results should be treated with caution [ 38 ]. In addition, our study found that RBPMS2, RBMX2, C1orf35, POU2AF1, FKBP5, FAM177B, CEP70, and PSRC1 are potential prognotic markers for CRC, however their mechanisms of action have not been investigated. T cells are critical to the efficacy of current tumor immunotherapy [ 20 ]. T cell exhaustion is a state of function diminishing characterized by a progressive loss of T cell effector function and self-renewal [ 39 ], which hampers immunotherapy of tumors. Blocking immune checkpoints can eliminate tumors by restoring immunity, thereby restoring dysfunctional/exhausted T cells [ 40 ]. In this study, a melanoma cohort receiving immunotherapy was validated [ 19 ], we found that the signature gene score model predicted the efficacy of the immune checkpoint inhibitor (ICIs), patients with a higher score having a better prognosis and better efficacy. This may be closely related to immune cells infiltrating and immune checkpoints. These findings indicate that the signature gene score model in this study is promising to be a new indicator for evaluating tumor immune microenvironment and ICIs efficacy. Nevertheless, the association between the signature gene score model and immunotherapy efficacy needs to be further validated in larger samples and the underlying mechanism needs to be further explored. Inevitably, there are some limitations in our research. Firstly, further experiments in vivo and mechanistic studies are needed to reveal the exact role of each signature gene. Secondly, the potential of the model to predict immunotherapy response was assessed only indirectly, as no mRNA expression data from CRC patients receiving immunotherapy was searched. Thirdly, the predictive power of the model needs to be evaluated based on external validation from prospective and large-scale clinical trials. 5. Conclusion In summary, we identified 16 T cell exhaustion-related exosome genes that may play an important role in the development and progression of CRC. The risk score model constructed based on these genes reflects the unique clinicopathological characteristics and immune microenvironment characteristics of CRC patients, and may promote the application of precision medicine in CRC by predicting prognosis and guiding immunotherapy. Abbreviations CRC Colorectal Cancer TME Tumor Microenvironment COAD Colonic Adenocarcinoma READ Rectum Adenocarcinoma TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus DEGs Differentially Expressed Genes ssGSEA single sample Gene Set Enrichment Analysis GO Gene Ontology KEGG Kyoto Encyclopaedia of Genes and Genomes HR hazard ratio ROC Receiver Operator Characteristic TIM Tumor Immune Microenvironment CNV Copy Number Variation SNV Single Nucleotide Variation TMB Tumor Mutation Burden AUC Area Under ROC Curve OS Overall Survival MSS Microsatellite Stabilized TDEs Tumor-derived Exosomes ncRNA non-coding RNA circRNA circular RNA PD-L1 Programmed Death Ligand-1 ICIs Immune Checkpoint Inhibitor. Declarations Ethics approval and consent to participate The study was approved by the ethical committee of The First People’s Hospital of Foshan. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing Interests The authors declare that there is no conflict of interest regarding the publication of this paper. Funding This research was funded by the 14th Five-Year Medical high level key specialty construction project of Foshan (FSGSP145001), The 2023 Foshan Municipal Science and Technology Bureau's Self-Funded Scientific and Technological Innovation Project (2320001006343). Author Contributions Yilin Wang: Conceptualization, Software, Writing-original draft. Peizhu Su: Visualization, Writing - review & editing. Qinghua Lu: Formal analysis. Huiwen Huang: Data curation. Zhaotao Li: Project administration, Funding acquisition. 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IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in immunology. 2021;12:687975. Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nature medicine. 2018;24:1545-9. Thommen DS, Schumacher TN. T Cell Dysfunction in Cancer. Cancer cell. 2018;33:547-62. Kim CG, Jang M, Kim Y, Leem G, Kim KH, Lee H, et al. VEGF-A drives TOX-dependent T cell exhaustion in anti-PD-1-resistant microsatellite stable colorectal cancers. Science immunology. 2019;4:eaay0555. Mashouri L, Yousefi H, Aref AR, Ahadi AM, Molaei F, Alahari SK. Exosomes: composition, biogenesis, and mechanisms in cancer metastasis and drug resistance. Molecular cancer. 2019;18:75. Zhao S, Mi Y, Guan B, Zheng B, Wei P, Gu Y, et al. Tumor-derived exosomal miR-934 induces macrophage M2 polarization to promote liver metastasis of colorectal cancer. 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NCKAP1L defects lead to a novel syndrome combining immunodeficiency, lymphoproliferation, and hyperinflammation. The Journal of experimental medicine. 2020;217:e20192275. Kilian M, Sheinin R, Tan CL, Friedrich M, Krämer C, Kaminitz A, et al. MHC class II-restricted antigen presentation is required to prevent dysfunction of cytotoxic T cells by blood-borne myeloids in brain tumors. Cancer cell. 2023;41:235-251. He P, Li K, Li SB, Hu TT, Guan M, Sun FY, et al. Upregulation of AKAP12 with HDAC3 depletion suppresses the progression and migration of colorectal cancer. International journal of oncology. 2018;52:1305-16. Song G, Xu S, Zhang H, Wang Y, Xiao C, Jiang T, et al. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. Journal of experimental & clinical cancer research. 2016;35(1):148. Ding Q, Hou Z, Zhao Z, Chen Y, Zhao L, Xiang Y. Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network. Frontiers in bioengineering and biotechnology. 2022;10:841034. Xing XL, Yao ZY, Xing C, Huang Z, Peng J, Liu YW. Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer. BMC medical genomics. 2021;14:116. Mudd TW, Jr., Lu C, Klement JD, Liu K. MS4A1 expression and function in T cells in the colorectal cancer tumor microenvironment. Cellular immunology; 2021;360:104260. Liu J, Yi J, Zhang Z, Cao D, Li L, Yao Y. Deoxyribonuclease 1-like 3 may be a potential prognostic biomarker associated with immune infiltration in colon cancer. Aging. 2021;13:16513-26. Lian W, Jin H, Cao J, Zhang X, Zhu T, Zhao S, et al. Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR. Cancer cell international. 2020;20:105. Kim HC, Chang J, Lee HS, Kwon HJ. Mitochondrial UQCRB as a new molecular prognostic biomarker of human colorectal cancer. Experimental & molecular medicine. 2017;49:e391. Liu D, Zhang H, Cui M, Chen C, Feng Y. Hsa-miR-425-5p promotes tumor growth and metastasis by activating the CTNND1-mediated β-catenin pathway and EMT in colorectal cancer. Cell cycle. 2020;19:1917-27. Chow A, Perica K, Klebanoff CA, Wolchok JD. Clinical implications of T cell exhaustion for cancer immunotherapy. Nature reviews Clinical oncology. 2022;19:775-90. Tsai HF, Hsu PN. Cancer immunotherapy by targeting immune checkpoints: mechanism of T cell dysfunction in cancer immunity and new therapeutic targets. Journal of biomedical science. 2017;24:35. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.png Supplementary Figure 1 The differences of CD20 in COAD and READ. (a) The differential expression of CD20 in COAD patients with different clinical features. (b) The differential expression of CD20 in READ patients with different clinical features. SupplementaryFigure2.tif Supplementary Figure 2 Differences in active, inhibit and two-side immune checkpoint genes expression between high and low Score groups. Supplementarytable1.txt Supplementarytable2.txt Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 26 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 18 Aug, 2024 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. 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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-4933597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345116913,"identity":"06f74888-17be-4bd3-87f6-8617675337b1","order_by":0,"name":"Yilin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFAC5sYHH//Z8PCzNxCthbHZcAZbmoxkzwHitbQJ87AdtjG44UCkBoMbiW0MPDzneRhuMDB++JhDpJYHEhK3eRhnNzBLztxGhBazG4ntBgYGt3mYZQ6wMfMSqaVNIiHhHA+bRAIpWg4cOMDDQ7QW+zMPmw0bG5J5JHgONhPnF8n25IOP/zbY2dsfbz744SMxWhgEEmAsxgZi1AMB/wEiFY6CUTAKRsHIBQCgbzio6zuFkgAAAABJRU5ErkJggg==","orcid":"","institution":"The First People’s Hospital of Foshan","correspondingAuthor":true,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Wang","suffix":""},{"id":345116915,"identity":"ae077799-bb6e-4e3d-8f30-48a798e75961","order_by":1,"name":"Peizhu Su","email":"","orcid":"","institution":"The First People’s Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Peizhu","middleName":"","lastName":"Su","suffix":""},{"id":345116917,"identity":"3d01c078-f4fa-4779-8e2a-401d3ea9d50a","order_by":2,"name":"Qinghua Lu","email":"","orcid":"","institution":"The First People’s Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Lu","suffix":""},{"id":345116919,"identity":"da9874e3-3e87-4383-99d6-ea9cb0e18f1e","order_by":3,"name":"Huiwen Huang","email":"","orcid":"","institution":"The First People’s Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Huiwen","middleName":"","lastName":"Huang","suffix":""},{"id":345116921,"identity":"5e2b6bbe-4eeb-4602-90c5-231d430b723a","order_by":4,"name":"Zhaotao Li","email":"","orcid":"","institution":"The First People’s Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Zhaotao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-18 13:46:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4933597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4933597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64599299,"identity":"360bea81-72ba-49ca-b79b-cbc472518320","added_by":"auto","created_at":"2024-09-16 11:38:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":654017,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot and scatter plot. \u003cstrong\u003e(a)\u003c/strong\u003e Volcano plot of DEGs between normal exosomes and colorectal cancer exosomes. \u003cstrong\u003e(b)\u003c/strong\u003e Volcano plot of DEGs between normal and tumor samples in TCGA-COADREAD. \u003cstrong\u003e(c)\u003c/strong\u003e Scatter plot of the correlation between expression of some TEX-exo genes and enrichment scores for T cell exhaustion.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/8500472f34e98b04cea5c77b.png"},{"id":64599300,"identity":"28f02999-483e-4510-b008-63661878a422","added_by":"auto","created_at":"2024-09-16 11:38:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1105286,"visible":true,"origin":"","legend":"\u003cp\u003eGO, KEGG enrichment results and Genetic expression variations.\u003cstrong\u003e (a)\u003c/strong\u003e GO and KEGG enrichment results of 179 TEX-exo genes. The horizontal axis represents the enrichment index, which is expressed as the Generatio; The size of the dot indicates the number of genes annotated to the pathway or the term, and the larger the dot indicates the more genes annotated; The color of the dot indicates the significance of the enrichment, with purple to red indicating low to high significance. \u003cstrong\u003e(b)\u003c/strong\u003e Mutation frequencies of some TEX-exo genes in colorectal cancer patients. Each column represents an individual patient, and the upper bar shows the tumor mutation burden (TMB); The number on the right represents the mutation frequency of each gene; The bar chart on the right shows the proportions of each variant type; The stacking bar graph below shows the conversion rates in each sample. \u003cstrong\u003e(c)\u003c/strong\u003e CNV frequency of some TEX-exo genes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/dc836301cf51912c1aad94eb.png"},{"id":64599301,"identity":"8b87e8c9-2b44-4409-a2e2-6d3982d2f754","added_by":"auto","created_at":"2024-09-16 11:38:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1614657,"visible":true,"origin":"","legend":"\u003cp\u003eThe signature model. \u003cstrong\u003e(a)\u003c/strong\u003e Nineteen TEX-exo genes associated with OS calculated by univariate COX analysis. \u003cstrong\u003e(b)\u003c/strong\u003e The survival curves of 8 TEX-exo genes with the most significant p value.\u003cstrong\u003e (c) \u003c/strong\u003eThe trajectory of each independent variable. The horizontal axis represents the log value of the independent variable lambda, and the vertical axis represents the coefficient of the independent variable. \u003cstrong\u003e(d)\u003c/strong\u003eConfidence intervals under each lambda. \u003cstrong\u003e(e)\u003c/strong\u003e Regression coefficients for 16 signature genes.\u003cstrong\u003e (f) \u003c/strong\u003eForest plot of the 16 signature genes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/5df45c2bf24b00ca32a0159a.png"},{"id":64599309,"identity":"143024e5-8e13-4362-a4ff-198592107dee","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":306717,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic model based on TCGA-COADREAD and GSE41258 dataset. \u003cstrong\u003e(a)\u003c/strong\u003e Distribution plots of Scores for each sample in the TCGA-COADREAD cohort.\u003cstrong\u003e (b)\u003c/strong\u003e Survival in each sample in the TCGA-COADREAD cohort. \u003cstrong\u003e(c)\u003c/strong\u003e Heat map of expression of the 16 signature genes in the high and low Score groups in the TCGA-COADREAD cohort.\u003cstrong\u003e(d) \u003c/strong\u003eSurvival curve of the high and low Score groups in the TCGA-COADREAD cohort. \u003cstrong\u003e(e)\u003c/strong\u003e Time-dependent ROC curve of Score in the TCGA-COADREAD cohort.\u003cstrong\u003e (f) \u003c/strong\u003eDistribution plots of Score for each sample in the GSE41258 cohort. \u003cstrong\u003e(g)\u003c/strong\u003e Survival in each sample in the GSE41258 cohort. \u003cstrong\u003e(h)\u003c/strong\u003eHeat map of expression of the 16 signature genes in the high and low Score groups in the GSE41258 cohort. \u003cstrong\u003e(i)\u003c/strong\u003e Survival curve of the high and low Score groups in the GSE41258 cohort. \u003cstrong\u003e(j) \u003c/strong\u003eTime-dependent ROC curve of Score in the GSE41258 cohort.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/205d11d37e99d24993a6f59a.png"},{"id":64599910,"identity":"4983e5d2-25e9-4402-ab11-484f7f9e1180","added_by":"auto","created_at":"2024-09-16 11:46:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1685443,"visible":true,"origin":"","legend":"\u003cp\u003eScore was an independent prognostic factor in the TCGA-COADREAD and GSE41258 cohort. \u003cstrong\u003e(a)\u003c/strong\u003e Univariate Cox analysis of the TCGA-COADREAD cohort. \u003cstrong\u003e(b) \u003c/strong\u003eMultivariate Cox analysis of the TCGA-COADREAD cohort.\u003cstrong\u003e (c)\u003c/strong\u003e Univariate Cox analysis of the GSE41258 cohort. \u003cstrong\u003e(d)\u003c/strong\u003e Multivariate Cox analysis of the GSE41258 cohort.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/c5df55ccb9f70ef511c23b2d.png"},{"id":64599306,"identity":"888a89bd-af37-45bc-8765-53bcfc8e6f9f","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":957904,"visible":true,"origin":"","legend":"\u003cp\u003eThe multivariate Nomogram with Score can predict the progression of CRC. \u003cstrong\u003e(a) \u003c/strong\u003eNomogram model including Score. \u003cstrong\u003e(b)\u003c/strong\u003e Calibration curve of the nomogram. \u003cstrong\u003e(c)\u003c/strong\u003eConstruction of the DCA decision curve.\u003cstrong\u003e (d)\u003c/strong\u003e Robustness analysis of Score in groups with different clinical characteristics. \u003cstrong\u003e(e)\u003c/strong\u003e Differences in Score between groups with different clinical characteristics.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/635c4c68bfd855e5708e1487.png"},{"id":64599911,"identity":"1943acb4-a35d-4601-9187-3b7d89f33dcf","added_by":"auto","created_at":"2024-09-16 11:46:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1426729,"visible":true,"origin":"","legend":"\u003cp\u003eSingle cell analysis based on Score. \u003cstrong\u003e(a)\u003c/strong\u003e Cell distribution before batch effect. \u003cstrong\u003e(b)\u003c/strong\u003e Cell distribution after debatching effect.\u003cstrong\u003e (c)\u003c/strong\u003e Distribution of cell types. \u003cstrong\u003e(d)\u003c/strong\u003e Distribution of Score.\u003cstrong\u003e (e) \u003c/strong\u003eDifferences in the distribution of Score across cell subsets. \u003cstrong\u003e(f) \u003c/strong\u003elog2FC of each cell subsets.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/537114e18c31f4fde20e1742.png"},{"id":64599310,"identity":"920528f7-b809-4786-a3ad-3dca0b02ab7a","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":511981,"visible":true,"origin":"","legend":"\u003cp\u003eThe application of Score in the immunotherapy cohort.\u003cstrong\u003e (a)\u003c/strong\u003e Boxplot of differences in immune cell infiltration between high and low Score groups.\u003cstrong\u003e (b) \u003c/strong\u003eSignificantly differentially expressed immune checkpoints between high and low Score groups. Gene name font colors green for active genes, red for inhibit genes, and orange for two-side genes.\u003cstrong\u003e (c)\u003c/strong\u003e Survival curves of the high and low Score groups in the immunotherapy cohort.\u003cstrong\u003e (d)\u003c/strong\u003e The proportion of responders and non-responders in high and low Score groups. \u003cstrong\u003e(e)\u003c/strong\u003e Differences in Score between groups of patients who responded to immunotherapy and those who did not. \u003cstrong\u003e(f)\u003c/strong\u003e Differences in TIDE scores between high and low score groups in TCGA-COADREAD.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/4371446092cbaa6f12a1509f.png"},{"id":64600478,"identity":"64aa4a94-f589-4567-ae47-d0641e78ed95","added_by":"auto","created_at":"2024-09-16 11:54:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8492934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/a498632f-b29b-4e4a-b8cb-e2c274d8df39.pdf"},{"id":64599311,"identity":"9371b77d-fb3b-429c-a685-ae6a1193ee95","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":807854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e The differences of CD20 in COAD and READ.\u003cstrong\u003e (a)\u003c/strong\u003e The differential expression of CD20 in COAD patients with different clinical features.\u003cstrong\u003e (b)\u003c/strong\u003e The differential expression of CD20 in READ patients with different clinical features.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/b64f825be62e396e6faf4c2e.png"},{"id":64599313,"identity":"65a28ed7-6320-4338-9105-55c4e8b7dca3","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29160418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e Differences in active, inhibit and two-side immune checkpoint genes expression between high and low Score groups.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/36dec6e962df5318832ffe94.tif"},{"id":64599909,"identity":"006b580f-d7a7-4789-b4d5-f61435934db2","added_by":"auto","created_at":"2024-09-16 11:46:25","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":258,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.txt","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/7c3947d44a4f60601eff2037.txt"},{"id":64599303,"identity":"e133cf88-f931-4693-9e0e-bfb33d6c12ce","added_by":"auto","created_at":"2024-09-16 11:38:26","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20623,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.txt","url":"https://assets-eu.researchsquare.com/files/rs-4933597/v1/83518aedc984ef8090bf8461.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) has the third highest incidence and mortality in the world nowadays, and is characterized by high heterogeneity and invasiveness, posing a serious threat to human health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. New prevention and treatment strategies are urgently needed. Clinical decisions on chemotherapy for CRC depend primarily on clinicopathological staging, regardless of molecular biological characteristics, and this inadequate decision-making approach may lead to potential over- or under-treatment. In the era of individualized therapy, it is imperative to identify reliable biomarkers to optimize the prognosis of colorectal cancer pharmacotherapy.\u003c/p\u003e \u003cp\u003eExosomes are tiny goblet vesicles secreted by cells. A growing number of studies have clarified the correlation between exosome production and tumorigenesis [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Tumor-derived exosomes participate in the exchange of genetic information between tumor cells and basal cells, affecting angiogenesis and promoting tumor growth, metastasis and invasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous pieces of evidence strongly indicate that exosomes play a pivotal regulatory role in the Tumor Microenvironment (TME) of CRC, influencing factors such as flora, hypoxia, inflammation, and the immunological microenvironment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As tumor markers for diagnosis and treatment, exosomes have become one of the focuses of current research. Exosome circSATB2 is associated with non-small cell lung cancer progression and therefore can potentially be used as a diagnostic marker for this cancer type [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Exosome circ0048117 regulates esophageal squamous cell carcinoma progression, and higher serum exosome circ0048117 is significantly and positively correlated with TNM stage [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, circulating exosome miR-203 levels correlate with metastasis, and low miR-203 expression in tumor tissues is a poor prognostic factor in colorectal cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn chronic infections or cancer, T cells gradually lose effector function and memory T cells begin to be depleted due to prolonged exposure to antigens and inflammation, this process is known as T Cell exhaustion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which is one of the major obstacles to anti-cancer immunotherapy. T cell exhaustion represents a unique state of T cell differentiation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], has considerable clinical relevance, but our understanding of it is still incomplete.\u003c/p\u003e \u003cp\u003eTo date, no comprehensive analysis of T cell exhaustion-related exosome prognostic models for colorectal cancer has been conducted. The biological process of T cell exhaustion-related exosomes in colorectal cancer and the relationship between them and clinical treatment and immunotherapy of patients are still unclear. Therefore, we systematically screened out the signature genes of T cell exhaustion-related exosomes in colorectal cancer based on the existing database data, and analyzed the correlation between the signature genes and clinical treatment and immunotherapy of patients.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eClinical information and expression data for colonic adenocarcinoma (COAD), rectum adenocarcinoma (READ) were obtained from The Cancer Genome Atlas of America database (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and were combined as a training set after removing the batch effects. Colorectal cancer exosome and normal exosome expression profiles were downloaded from the exoRBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.exorbase.org/\u003c/span\u003e\u003cspan address=\"http://www.exorbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The set GSE41258 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41258\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41258\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was downloaded from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a validation set, and the set GSE132465 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a single cell quality control data set. T cell exhaustion related genes were extracted from the study of Zhang et al [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and shown in Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Screening of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eDEGs were filtered through the \u0026ldquo;\u003cem\u003elimma\u003c/em\u003e \u0026rdquo; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] package. The DEGs threshold point was an adjusted \u003cem\u003ep\u003c/em\u003e value, corrected by the BH method, less than 0.05 and a |log\u003csub\u003e2\u003c/sub\u003e fold change| greater than 0.585.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Single sample gene set enrichment analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eEnrichment analysis for the expression profile of TCGA-COADREAD tumor samples was performed by R package \u0026ldquo;GSVA\u0026rdquo; [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] based on 40 T cell exhaustion-related genes obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Screening of TEX-exo genes\u003c/h2\u003e \u003cp\u003eThe intersection of the two groups of DEGs was named \u0026ldquo;exo-genes\u0026rdquo;. Spearman correlation between exo-genes expression and T cell exhaustion-related enrichment score was calculated in the training set, and the significantly related genes were retained as TEX-exo genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eGene ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses and top10 visual display of the TEX-exo genes were performed using the R package \u0026ldquo;clusterProfiler\u0026rdquo; [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the parameters were pvalueCutoff\u0026thinsp;=\u0026thinsp;0.05, pAdjustMethod = \u0026ldquo;BH\u0026rdquo;, and qvalueCutoff\u0026thinsp;=\u0026thinsp;0.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Construction of the Signature model\u003c/h2\u003e \u003cp\u003eThe hazard ratio (HR) and prognostic significance of TEX-exo genes were determined by univariate Cox regression analysis. The genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were prognostic related genes. The LASSO algorithm was implemented through the R package \u0026ldquo;glmnet\u0026rdquo; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for further selection and contraction of prognostic related genes. The selected genes were the signature genes. Then calculated the risk scores:\u003c/p\u003e \u003cp\u003eScore = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=0}^{n}\\beta\\:i\\text{*}\\chi\\:i\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003en for the number of signature genes, \u0026#120573;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e for the weight coefficient of each gene, \u0026#120594;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e for the expression of each gene.\u003c/p\u003e \u003cp\u003ePatients were divided into high - and low-risk groups based on the median Score, the \u0026ldquo;survminer\u0026rdquo; package was used to analyze the OS and the \"timeROC\" package was used to plot a time-dependent receiver operator characteristic (ROC) curve. Finally, univariate and multivariate Cox analyses were performed to explore the independent prognostic value of risk Scores and to validate them in the validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Nomogram construction\u003c/h2\u003e \u003cp\u003eA nomogram including clinical features and risk Scores was constructed using the R package \u0026ldquo;rms\u0026rdquo;, and a calibration curve was then drawn to assess the accuracy of the nomogram in predicting survival rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Single-cell analysis\u003c/h2\u003e \u003cp\u003eR package \"Seurat\" [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used for quality control of colorectal cancer samples to exclude low-quality cells and low-expression genes, and the Score of each cell was calculated according to the formula above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Evaluation of tumor immune microenvironment (TIM)\u003c/h2\u003e \u003cp\u003eCIBERSORTx algorithm in R package \u0026ldquo;IOBR\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to calculate the Scores of 22 immune cells in tumor immune microenvironment in the training set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses in this paper were carried out by R version 4.1.2. The Wilcoxon rank sum test was used to analyze the significance of two groups of numerical variables, and Kruskal\u0026thinsp;\u0026minus;\u0026thinsp;Wallis test for more than two groups. In the plots display, ns means p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, * means p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** means p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** means p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **** means p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Survival curves were generated by Kaplan-Meier method, and the significance of the differences was determined by log-rank test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening of TEX-exo genes\u003c/h2\u003e \u003cp\u003eThe DEGs between normal exosome and colorectal cancer exosome samples was calculated by the \u0026ldquo;limma\u0026rdquo; algorithm, and 1016 DEGs were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The expression difference of normal and tumor samples in TCGA-COADREAD was calculated by the same method, and 4352 DEGs were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Taking the intersection of the DEGs of the above two resulted in 276 differential exo-genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe T cell exhaustion-related enrichment scores for each sample in TCGA-COADREAD are generated based on 40 T cell exhaustion-related genes with the \"GSVA\" algorithm, as detailed in Supplementary Table\u0026nbsp;2. Spearman correlation was calculated between the expression of 276 exo-genes and T cell exhaust-related enrichment score in TCGA-COADREAD, and 179 genes with significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained as the TEX-exo genes for further analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec shows 8 genes with the highest correlation among the genes with significant positive correlation, including DOCK2, LSP1, HCLS1, NCKAP1L, GIMAP1, ARHGEF6, GYPC and ARHGAP25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo understand the biological mechanisms related to T cell exhaustion in exosomes, KEGG and GO enrichment of TEX-exo genes were analyzed, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. The biological process of GO enrichment showed that these TEX-exo genes were mainly distributed in the lymphocyte proliferation, mononuclear cell proliferation, leukocyte proliferation, and so forth. The celluar component of GO enrichment suggested that these TEX-exo genes were involved in the cell cortex, secrebory granule lumen, spliceosomal snRhP complex and so on. The molecular function of GO enrichment indicated that these TEX-exo genes enriched in the carbohydrate binding, MHC protein complex binding and other functions. In addition, the KEGG results demonstrated that these TEX-exo genes enriched in Hematopoietic cell lineage, Intestinal immune network for IgA production and other pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further confirm the function of proteins encoded by TEX-exo genes, we download COAD and READ level 4 protein expression data from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tcpaportal.org/tcpa/\u003c/span\u003e\u003cspan address=\"https://www.tcpaportal.org/tcpa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Among the 179 TEX-exo genes, only the MS4A1 gene was able to correspond to its matching encoded protein, CD20. Therefore, We further compared the differences of CD20 in six different clinical feature groups (age, sex, stage, colon polyps, lymphatic invasion and venous invasion) of COAD and READ. There were no significant differences in all six groups. The results can be seen in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eTo gain further insight into the genetic variation of the TEX-exo genes, we also analyzed the incidence of copy number variation (CNV) and somatic mutations in the 179 TEX-exo genes. First of all, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb showed the genes with single nucleotide variation (SNV) frequency above 3%. Among them, DOCK2 exhibited the highest mutation frequency, followed by AKAP12. Then, investigation of the frequency of CNV revealed that CNV was widespread in the 179 TEX-exo genes, for example, there were extensive amplification in IGF2 and MMP9, and extensive deletion in TCEA3 and STMN1, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. This systematically revealed genetic variation in TEX-exo genes, illustrating the potential pathogenic landscape of these genes in CRC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Building the signature model\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression analysis was used to evaluate the effect of TEX-exo genes on OS. Nineteen genes were found to be significant prognostic factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Of these genes, seven (HR\u0026thinsp;\u0026gt;\u0026thinsp;1; RBPMS2, AKAP12, SCARA3, TIMP1, HSPA1A, RBMX2 and C1orf35) were risk factors for OS and 12 (HR\u0026thinsp;\u0026lt;\u0026thinsp;1; PPP1R16B, MS4A1, POU2AF1, BIRC3, FKBP5, DNASE1L3, CTNND1, FAM177B, SULT1B1, CEP70, UQCRFS1 and PSRC1) were protective factors for OS. Survival analysis was performed on these 19 prognostic factors in tumor samples, and the median expression level was used as the cut-off value to divide the high and low expression groups to compare the influence of expression level on prognosis. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb showed the survival curves of 8 genes with the most significant p value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There are 4 genes with low expression that have a better prognosis (HR\u0026thinsp;\u0026lt;\u0026thinsp;1; HSAP1A, RBMX2, C1orf35 and SCARA3). On the contrary, 4 genes with high expression have a superior prognosis (HR\u0026thinsp;\u0026gt;\u0026thinsp;1; UQCRFS1, MS4A1, FAM177B and CEP70).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the prognostic value of the 19 genes, we would like to build a more reliable and useful predictive model from these genes to guide clinical practice in colorectal cancer patients. The expression profiles of the 19 genes above were analyzed by LASSO-Cox regression analysis, and the prognostic model was constructed. 16 signature genes were identified based on the optimal value of λ 0.0087, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-e. Furthermore, the regression coefficients of each signature gene were shown in Table\u0026nbsp;1. The results of univariate COX analysis of these 16 signature genes in OS are displayed in the forest plot of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, and all of their expressions were strongly associated with prognosis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1\u003c/b\u003e Regression coefficients for 16 signature gene.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1orf35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.619111702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.281871688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTNND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.215980486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSPA1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.170078448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBMX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1222027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEP70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.088315003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS4A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03913604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOU2AF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032869455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBPMS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004424399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKAP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.010104709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNASE1L3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0677533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSULT1B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08324181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM177B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.089640959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUQCRFS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.151520918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFKBP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.206617298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.401296281\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 Based on the coefficients calculated for each gene by the LASSO-COX algorithm and the expression values of the 16 signature genes for each CRC patient, we were able to compute the Score for each patient according to the formula in the Methods section. Patients were divided into two groups with high Score and low Score according to the median value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Survival times for each high Score patient and low Score patient were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. Overall, patients with high Score had the shorter survival time and those with low Score had the longer survival time. Moreover, the expression of the 16 signature genes in patients with high and low Score was exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec. Kaplan-Meier curve showed that the survival probability of patients with higher Scores was significantly lower than that of patients with lower Scores (HR\u0026thinsp;=\u0026thinsp;2.97; 95% CI\u0026thinsp;=\u0026thinsp;1.98\u0026ndash;4.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The area under ROC curve (AUC) was 0.712 in year 1, 0.676 in year 3, and 0.721 in year 5, which means that this risk scoring model has a high value for prognostic guidance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to verify the stability of the model, the same algorithm was used to analyze the verification set (GSE41258). Similarly, the validation set patients were categorized into high and low Score based on the median Score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), whereas the survival time of the high Score patients remained longer overall, while the survival time of the low Score patients continued to be shorter on the whole (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). In addition, the expression of 16 signature genes in high and low Score patients was demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh. Consistent with results obtained in the training set TCGA-COADREAD cohort, patients in the higher Score group had a shorter survival time compared to those in the lower Score group (HR\u0026thinsp;=\u0026thinsp;1.75; 95% CI\u0026thinsp;=\u0026thinsp;1.18\u0026ndash;2.61; P\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei). In addition, in terms of survival effect prediction, the 1-year AUC was 0.724, 3-year AUC was 0.640, and 5-year AUC was 0.603, which reaffirmed the excellent efficacy of this risk score model in predicting prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003eIn order to exclude the influence of other factors, univariate and multivariate Cox analyses were used to determine whether Score was an independent prognostic factor for OS. In univariate Cox analysis, Score obtained by the training set TCGA-COADREAD cohort was significantly correlated with OS (High vs. Low; HR\u0026thinsp;=\u0026thinsp;2.97, 95% CI\u0026thinsp;=\u0026thinsp;1.98\u0026ndash;4.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). After adjusting for other confounding factors, multivariate Cox analysis showed that Score was still an independent predictor of OS (High vs. Low; HR\u0026thinsp;=\u0026thinsp;3.47, 95% CI\u0026thinsp;=\u0026thinsp;1.74\u0026ndash;6.93, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, univariate and multivariate Cox analysis was used in the verification set. In univariate Cox analysis, Score obtained by the validation set cohort was significantly correlated with OS (High vs. Low; HR\u0026thinsp;=\u0026thinsp;1.75, 95% CI\u0026thinsp;=\u0026thinsp;1.18\u0026ndash;2.61, P\u0026thinsp;=\u0026thinsp;0.006; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). After adjusting for other confounding factors, multivariate Cox analysis showed that Score was still an independent predictor of OS (High vs. Low; HR\u0026thinsp;=\u0026thinsp;1.52, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;2.29, P\u0026thinsp;=\u0026thinsp;0.043), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Nomogram construction\u003c/h2\u003e \u003cp\u003eWe combined Score with five different clinical features (age, sex, stage, lymphatic invasion and venous invasion) to construct a nomogram to predict the probability of 1-year, 3-year, and 5-year OS. Each factor was assigned in proportion to its risk contribution to survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), and calibration curves showed that the combined model (nomogram) showed high accuracy over 1 -, 3 -, and 5-year OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Compared with a single prognostic factor, the nomogram constructed using a combination model may be a better predictor of patients' OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). We analyzed the robustness of Score in different clinical features, and the results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed. In most clinical groups, we observed that the prognosis of the patients with low Scores was better than that of the patients with high Scores (HR\u0026thinsp;\u0026lt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We also compared the differences in Scores among six different clinical feature groups, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee. Scores were higher in stage III/IV patients than in stage I/II patients, in patients with lymphatic invasion than in patients without lymphatic invasion, and in patients with venous invasion than in patients without venous invasion. Additionally, there were no significant differences in Scores among different age, sex and colon polyps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Single-cell analysis\u003c/h2\u003e \u003cp\u003eThe role and value of the Score was further validated using the single-cell analysis of colorectal cancer. The single-cell analysis of 23 colorectal cancer tumor samples showed that 40,000 cells were retained from the original 47,285 cells after quality control. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb showed the landscape of cell distribution before batch effect and after debatching effect, respectively. The distribution of the six cell types (B cells, Epithelial cells, Mast cells, Myeloids, Stromal cells and T cells) can be derived from the gene expression values of each cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). The same formula was used to calculate the Score of each cell, and the results were displayed by using UMAP, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed. Among the six cell types, Stromal cells had the highest Scores, while T cells had the lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). The differences in Scores between the other five cells and the Epithelial cells were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef. Among them, compared with Epithelial cells, the Scores of Mast cells, Myeloids and Stromal cells were relatively high (log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0), while the Scores of B cells and T cells were relatively low (log2FC\u0026thinsp;\u0026lt;\u0026thinsp;0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Evaluation of TME\u003c/h2\u003e \u003cp\u003eWe further explored the tumor immune microenvironment between the high and low Scores groups with the aim of identifying the underlying immune mechanisms. Different immune cell subsets were quantified using CIBERSORTx, and rank sum tests were used to compare the significance of infiltration degree between groups. We found that the infiltration degree of B cells memory, Dendritic cells activated Macrophages M0 cells, Plasma cells, T cells CD4 memory resting, and T cells regulatory were significantly different in higer and lower Score groups. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also investigated the differences in the expression of immune checkpoints in the higher and lower Score groups. Expression of all active, inhibit and two-side immune checkpoint genes differed significantly between high and low Scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb and Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eWe also explored whether prognostic signature could predict a patient's response to immune checkpoint blocking therapy. Immunotherapy data for a metastatic melanoma was used [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], it was found that patients with high Scores have a better prognosis than those with low Scores (P\u0026thinsp;=\u0026thinsp;0.038; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Furthermore, patients who responded to immunotherapy drugs had higher Scores than those who did not respond to the drugs (P\u0026thinsp;=\u0026thinsp;0.026; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). Among the high Scores patients, 28.57% responded to immunotherapy, while 71.43% did not. In addition, all patients in the low Scores patients did not respond to immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). Finally, the high Scores group had higher TIDE scores than the low Scores group in TCGA-COADREAD (P\u0026thinsp;=\u0026thinsp;3.9e-11; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef). In conclusion, the immunotherapy effect of the high Scores group was better than that of the low Scores group. Therefore, Scores can be used as a novel marker to guide immunotherapy in colorectal cancer patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eT cell exhaustion is critical in tumor immunotherapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies show that severe exhaustion of tumor-infiltrating T cells in microsatellite stabilized (MSS) colorectal cancer is one of the important mechanisms by which patients develop resistance to PD-1 inhibitors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Tumor-derived exosomes (TDEs) are involved in various processes of cancer formation and development, including tumor microenvironment remodeling, angiogenesis, invasion, metastasis, and drug resistance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It has been found that TDEs can promote the occurrence of liver metastasis in CRC by regulating the crosstalk between tumor cells and macrophages [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To our knowledge, this study is the first to systematically evaluate T cell exhaustion-related exosome genes in CRC. Firstly, a risk score model containing 16 signature genes was established through the TCGA cohort, and a nomogram was constructed to predict the OS of CRC patients, while the model was validated using an external data set. Secondly, the GSE132465 data set was used to characterize the features of the scores of signature genes in single cells. Finally, we also found that signature gene scores were associated with other clinicopathologic features and tumor immune microenvironment in patients with colorectal cancer.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that non-coding RNA (ncRNA) and circular RNA (circRNA) derived from exosome can affect the tumor immune microenvironment, including inducing T cell exhaustion and promoting the overexpression of programmed death ligand-1 (PD-L1) [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, considering that the cross-talk between mRNA in exosomes and T cell exhaustion on CRC needs to be explored, we identified 179 TEX-exo genes, which are not only DEGs of CRC exosomes versus normal exosomes, but also DEGs closely related to T cell exhaustion. Eight exosomal genes, including DOCK2, LSP1, HCLS1, NCKAP1L, GIMAP1, ARHGEF6, GYPC and ARHGAP25, were significantly positively correlated with T cell exhaustion (R\u0026thinsp;\u0026gt;\u0026thinsp;0.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It has been reported that cholesterol sulfate synthesized by SULT2B1 in hepatocellular carcinoma inhibit DOCK2 activity in T cells and promote effector T cell exhaustion [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, a combination of RNA-seq and proteomics analysis of human NCKAP1L deficiency cases reported by Castro et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] showed characteristics of T cell exhaustion. 179 TEX-exo genes were mainly concentrated in immune-related molecules or signaling pathways, such as MHC class II protein complex, lymphocyte proliferation and Intestinal immune network for IgA production. Kilian et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] showed that MHC Class II restricted antigen presentation is required to prevent cytotoxic T cell dysfunction in brain tumors. In addition, despite the prevalence of CNV in 179 TEX-exo genes, the relationship between T cell exhaustion and CNV remains unclear, further studies are needed.\u003c/p\u003e \u003cp\u003eWe constructed a signature gene scoring model based on 16 T cell exhaustion-related exosome genes. Signature genes scores in both the TCGA cohort and GSE41258 showed excellent prognostic ability and were identified as an independent risk factors for OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with a single risk score model, a nomogram with signature genes scores and clinicopathological features is able to predict patients' OS more accurately. In addition, the current signature genes risk score is associated with some clinicopathologic features, including TNM staging, lymph node invasion, and vascular invasion.\u003c/p\u003e \u003cp\u003eAmong the 16 signature genes, RBPMS2, AKAP12, TIPM1, HSPA1A, RBMX2 and C1orf35 were negatively correlated with CRC patients' OS (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), while MS4A1, POU2AF1, FKBP5, DNASE1L3, CTNND1, FAM177B, SULT1B1, CEP70, UQCRFS1 and PSRC1 were positively correlated with OS in patients with colorectal cancer (HR\u0026thinsp;\u0026lt;\u0026thinsp;1). He et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported that down-regulation of AKAP12 can inhibit the progression and migration of CRC through the PI3K/AKT signaling pathway. It was reported that TIMP1 expression was significantly associated with regional lymph nodes and distant metastases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The down-regulation of HSPA1A inhibits the proliferation and migration of CRC cells, and CRC patients with lower expression levels tend to have longer OS [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MS4A1, DNASE1L3 and SULT1B1 were significantly down-regulated in CRC tissues, and their reducing levels were associated with shorter OS in CRC patients [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. UQCRB plays a key role in mitochondrial complex III stability, electron transport, cellular oxygen sensing and angiogenesis, and can be used as an important diagnostic marker for CRC [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although our study showed a positive correlation between CTDNN1 and OS, some basic medicine studies suggested that the activation of CTDNN1 could induce the proliferation and migration of CRC cells, so the experimental results should be treated with caution [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, our study found that RBPMS2, RBMX2, C1orf35, POU2AF1, FKBP5, FAM177B, CEP70, and PSRC1 are potential prognotic markers for CRC, however their mechanisms of action have not been investigated.\u003c/p\u003e \u003cp\u003eT cells are critical to the efficacy of current tumor immunotherapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. T cell exhaustion is a state of function diminishing characterized by a progressive loss of T cell effector function and self-renewal [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which hampers immunotherapy of tumors. Blocking immune checkpoints can eliminate tumors by restoring immunity, thereby restoring dysfunctional/exhausted T cells [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In this study, a melanoma cohort receiving immunotherapy was validated [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], we found that the signature gene score model predicted the efficacy of the immune checkpoint inhibitor (ICIs), patients with a higher score having a better prognosis and better efficacy. This may be closely related to immune cells infiltrating and immune checkpoints. These findings indicate that the signature gene score model in this study is promising to be a new indicator for evaluating tumor immune microenvironment and ICIs efficacy. Nevertheless, the association between the signature gene score model and immunotherapy efficacy needs to be further validated in larger samples and the underlying mechanism needs to be further explored.\u003c/p\u003e \u003cp\u003eInevitably, there are some limitations in our research. Firstly, further experiments in vivo and mechanistic studies are needed to reveal the exact role of each signature gene. Secondly, the potential of the model to predict immunotherapy response was assessed only indirectly, as no mRNA expression data from CRC patients receiving immunotherapy was searched. Thirdly, the predictive power of the model needs to be evaluated based on external validation from prospective and large-scale clinical trials.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, we identified 16 T cell exhaustion-related exosome genes that may play an important role in the development and progression of CRC. The risk score model constructed based on these genes reflects the unique clinicopathological characteristics and immune microenvironment characteristics of CRC patients, and may promote the application of precision medicine in CRC by predicting prognosis and guiding immunotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColorectal Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColonic Adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRectum Adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle sample Gene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopaedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operator Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Immune Microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy Number Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Mutation Burden\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under ROC Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrosatellite Stabilized\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTDEs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-derived Exosomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003encRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecircRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecircular RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-L1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgrammed Death Ligand-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune Checkpoint Inhibitor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethical committee of The First\u0026nbsp;People’s Hospital of Foshan.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the 14th Five-Year Medical high level key specialty construction project of Foshan (FSGSP145001), The 2023 Foshan Municipal Science and Technology Bureau's Self-Funded Scientific and Technological Innovation Project (2320001006343).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYilin Wang:\u0026nbsp;Conceptualization, Software,\u0026nbsp;Writing-original draft.\u0026nbsp;Peizhu Su:\u0026nbsp;Visualization, Writing - review \u0026amp; editing. Qinghua Lu:\u0026nbsp;Formal analysis. Huiwen Huang:\u0026nbsp;Data curation. Zhaotao Li:\u0026nbsp;Project administration, Funding acquisition.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics. CA Cancer J Clin. 2021; 71:7-33.\u003c/li\u003e\n\u003cli\u003eHessvik NP, Llorente A. Current knowledge on exosome biogenesis and release. Cell Mol Life Sci. 2018;75:193-208.\u003c/li\u003e\n\u003cli\u003eLi X, Corbett AL, Taatizadeh E, Tasnim N, Little JP, Garnis C, et al. Challenges and opportunities in exosome research-Perspectives from biology, engineering, and cancer therapy. APL bioengineering 2019;3:011503.\u003c/li\u003e\n\u003cli\u003eSedgwick AE, D\u0026apos;Souza-Schorey C. 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IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in immunology. 2021;12:687975.\u003c/li\u003e\n\u003cli\u003eAuslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nature medicine. 2018;24:1545-9.\u003c/li\u003e\n\u003cli\u003eThommen DS, Schumacher TN. T Cell Dysfunction in Cancer. Cancer cell. 2018;33:547-62.\u003c/li\u003e\n\u003cli\u003eKim CG, Jang M, Kim Y, Leem G, Kim KH, Lee H, et al. VEGF-A drives TOX-dependent T cell exhaustion in anti-PD-1-resistant microsatellite stable colorectal cancers. Science immunology. 2019;4:eaay0555.\u003c/li\u003e\n\u003cli\u003eMashouri L, Yousefi H, Aref AR, Ahadi AM, Molaei F, Alahari SK. Exosomes: composition, biogenesis, and mechanisms in cancer metastasis and drug resistance. Molecular cancer. 2019;18:75.\u003c/li\u003e\n\u003cli\u003eZhao S, Mi Y, Guan B, Zheng B, Wei P, Gu Y, et al. Tumor-derived exosomal miR-934 induces macrophage M2 polarization to promote liver metastasis of colorectal cancer. Journal of hematology \u0026amp; oncology. 2020;13:156.\u003c/li\u003e\n\u003cli\u003eXu Z, Chen Y, Ma L, Chen Y, Liu J, Guo Y, et al. Role of exosomal non-coding RNAs from tumor cells and tumor-associated macrophages in the tumor microenvironment. Molecular therapy : the journal of the American Society of Gene Therapy. 2022;30:3133-54.\u003c/li\u003e\n\u003cli\u003eYang C, Wu S, Mou Z, Zhou Q, Dai X, Ou Y, et al. Exosome-derived circTRPS1 promotes malignant phenotype and CD8+ T cell exhaustion in bladder cancer microenvironments. Molecular therapy : the journal of the American Society of Gene Therapy. 2022;30:1054-70.\u003c/li\u003e\n\u003cli\u003eWang J, Zhao X, Wang Y, Ren F, Sun D, Yan Y, et al. circRNA-002178 act as a ceRNA to promote PDL1/PD1 expression in lung adenocarcinoma. Cell death \u0026amp; disease. 2020;11(1):32.\u003c/li\u003e\n\u003cli\u003eWang S, Wang R, Xu N, Wei X, Yang Y, Lian Z, et al. SULT2B1-CS-DOCK2 axis regulates effector T cell exhaustion in hepatocellular carcinoma microenvironment. Hepatology. 2023;78:1064-1078.\u003c/li\u003e\n\u003cli\u003eCastro CN, Rosenzwajg M, Carapito R, Shahrooei M, Konantz M, Khan A, et al. NCKAP1L defects lead to a novel syndrome combining immunodeficiency, lymphoproliferation, and hyperinflammation. The Journal of experimental medicine. 2020;217:e20192275.\u003c/li\u003e\n\u003cli\u003eKilian M, Sheinin R, Tan CL, Friedrich M, Kr\u0026auml;mer C, Kaminitz A, et al. MHC class II-restricted antigen presentation is required to prevent dysfunction of cytotoxic T cells by blood-borne myeloids in brain tumors. Cancer cell. 2023;41:235-251.\u003c/li\u003e\n\u003cli\u003eHe P, Li K, Li SB, Hu TT, Guan M, Sun FY, et al. Upregulation of AKAP12 with HDAC3 depletion suppresses the progression and migration of colorectal cancer. International journal of oncology. 2018;52:1305-16.\u003c/li\u003e\n\u003cli\u003eSong G, Xu S, Zhang H, Wang Y, Xiao C, Jiang T, et al. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. Journal of experimental \u0026amp; clinical cancer research. 2016;35(1):148.\u003c/li\u003e\n\u003cli\u003eDing Q, Hou Z, Zhao Z, Chen Y, Zhao L, Xiang Y. Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network. Frontiers in bioengineering and biotechnology. 2022;10:841034.\u003c/li\u003e\n\u003cli\u003eXing XL, Yao ZY, Xing C, Huang Z, Peng J, Liu YW. Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer. BMC medical genomics. 2021;14:116.\u003c/li\u003e\n\u003cli\u003eMudd TW, Jr., Lu C, Klement JD, Liu K. MS4A1 expression and function in T cells in the colorectal cancer tumor microenvironment. Cellular immunology; 2021;360:104260.\u003c/li\u003e\n\u003cli\u003eLiu J, Yi J, Zhang Z, Cao D, Li L, Yao Y. Deoxyribonuclease 1-like 3 may be a potential prognostic biomarker associated with immune infiltration in colon cancer. Aging. 2021;13:16513-26.\u003c/li\u003e\n\u003cli\u003eLian W, Jin H, Cao J, Zhang X, Zhu T, Zhao S, et al. Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR. Cancer cell international. 2020;20:105.\u003c/li\u003e\n\u003cli\u003eKim HC, Chang J, Lee HS, Kwon HJ. Mitochondrial UQCRB as a new molecular prognostic biomarker of human colorectal cancer. Experimental \u0026amp; molecular medicine. 2017;49:e391.\u003c/li\u003e\n\u003cli\u003eLiu D, Zhang H, Cui M, Chen C, Feng Y. Hsa-miR-425-5p promotes tumor growth and metastasis by activating the CTNND1-mediated \u0026beta;-catenin pathway and EMT in colorectal cancer. Cell cycle. 2020;19:1917-27.\u003c/li\u003e\n\u003cli\u003eChow A, Perica K, Klebanoff CA, Wolchok JD. Clinical implications of T cell exhaustion for cancer immunotherapy. Nature reviews Clinical oncology. 2022;19:775-90.\u003c/li\u003e\n\u003cli\u003eTsai HF, Hsu PN. Cancer immunotherapy by targeting immune checkpoints: mechanism of T cell dysfunction in cancer immunity and new therapeutic targets. Journal of biomedical science. 2017;24:35.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"T cell exhaustion, exosome, colorectal cancer, tumor immune microenvironment, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-4933597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4933597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTreatment options for colorectal cancer are limited. T cell exhaustion is one of the barriers to tumor immunotherapy. No comprehensive analysis of T cell exhaustion-related exosome prognostic models for colorectal cancer (CRC) has been conducted.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eSamples were collected from the Cancer Genome Atlas (TCGA) database, exoRBase database and Gene Expression Omnibus (GEO) database. The single sample gene set enrichment analysis (ssGSEA) algorithm screened out T cell exhaustion-related exosome differential expression genes, signature genes were screened by univariate Cox regression and Lasso regression, and risk score models were constructed and validated. A nomogram containing risk scores and clinical parameters was established and evaluated. In addition, single cell analysis and tumor immune microenvironment assessment were also performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixteen signature genes were identified, based on which the risk score model was constructed and validated. This model can predict the overall survival (OS) of TCGA and GEO queues well. Scores were identified as independent risk factors for OS and correlated with certain clinicopathological features. A nomogram was developed that integrated clinical parameters and risk scores and showed higher predictive accuracy. Finally, significant differences in immune microenvironment were found between the high- and low-risk groups. Thus, scores can also be used to predict the response to immunotherapy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn general, we screened out T cell exhaustion-related exosome genes of CRC, constructed a risk score model which could predict survival and immunotherapy efficacy, and found correlations between risk scores and clinicopathologic features and immune microenvironment.\u003c/p\u003e","manuscriptTitle":"T cell exhaustion-related exosome genes for predicting survival and immunotherapy efficacy in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 11:38:21","doi":"10.21203/rs.3.rs-4933597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-08-26T09:04:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-19T07:24:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2024-08-18T13:44:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4cc9758-682d-4a28-ad95-780116e556f7","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T11:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 11:38:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4933597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4933597","identity":"rs-4933597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}