Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing

Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing Wei Zhang, Xiaoming Zhu, Rongbo Wen, Jiaqi Wu, Leqi Zhou, Hao Fan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909225/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: T cells, the key mediators of tumor destruction, have a considerable impact on tumor prognosis. However, the clinical significance of T cell-associated biomarkers in colorectal cancer (CRC) haven’t been well understood. The aim of this study was to investigate the expression profile of T cell marker genes in CRC and develop a prognostic signature based on these genes. Methods: Single-cell RNA-sequencing (scRNA-seq) data were retrieved from the Gene Expression Omnibus (GEO) database. Bulk RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases. We firstly conducted a comprehensive analysis of scRNA-seq data to investigate the heterogeneity of various cells in the CRC tumor microenvironment (TME). Then, we performed cell-cell communication analysis and cell trajectory analysis to explore the intercellular interactions and functional changes of T cells. By combing the bulk RNA-seq data, a T-cell related gene signature was eventually constructed and its predictive ability was determined by the Kaplan–Meier (K-M), and receiver operating characteristic (ROC) curves in three independent cohorts. Results: ScRNA-seq data obtained from the GEO database were re-integrated and analyzed, resulting in 23 cell clusters. Distinct cell clusters were annotated using extensively reported cell markers. The CellChat algorithm revealed that tumor cells suppress the cellular function of tumor-infiltrating T cells through the MIF/CD74 pathway. The evolutionary trajectory of tumor-infiltrating T cells was elucidated by the CytoTRACE and monocle2 algorithms. Eventually, a prognostic prediction model based on 5 T cell-related genes was constructed using single-cell and bulk RNA sequencing data. The validation results from several independent CRC cohorts indicated that the 5 T cell-related genes prognostic model could accurately predict the survival outcomes of CRC patients, providing new evidence for precision treatment in CRC. Conclusions: Our study not only offers prospects for a better understanding of the cellular heterogeneity of TME, but also provides a useful tool for stratifying patients with different prognoses and facilitating personalized treatment. Biological sciences/Cancer/Cancer genomics Biological sciences/Cancer/Gastrointestinal cancer Colorectal cancer Single-cell RNA sequencing Bulk RNA-sequencing T cell marker gene Prognostic signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Colorectal cancer (CRC) is a common and deadly malignancy of the digestive system, with an estimated 153,020 new cases and 52,550 deaths in 2023 [ 1 ]. Despite marked advances in screening programs and medical therapies, survival rates of CRC have barely increased over the past several decades [ 2 ]. The lack of effective prognostic indicators to guide personalized treatment has greatly hindered the significant improvement of patient outcomes [ 3 ]. Due to the high biological heterogeneity and complex pathogenesis of CRC, existing prognostic predictors are insufficient to capture individual survival differences [ 4 – 6 ]. In this context, there is an urgent need to develop more accurate molecular classifiers to assess prognosis. Previous prognostic models mainly focus on cell-autonomous changes of CRC, based on biological processes occurred in cancer cells such as senescence, metabolism and cuproptosis [ 7 – 9 ], and few studies focus on the tumor microenvironment (TME). In solid cancer, cancerous cells are surrounded by the TME, an important and complex structure that comprises stromal cells, diversity of immune cells, fibroblasts and various cytokines [ 10 ]. Great attention has been drawn on recognizing the crucial role of TME in tumor initiation and progression. Accumulating evidence indicates that abnormal changes in the immune component of TME can not only regulate cancer progression but also affect patient survival, making the immune microenvironment an under-explored source of prognostic markers [ 11 – 13 ]. Among immune cells, T cells are the key mediators of anti-tumor immunity, and T cell-based immunotherapies have emerged as new therapeutic pillars within oncology [ 14 – 16 ]. Pre-clinical and clinical studies have confirmed that CD4 + and CD8 + T cells possess cytotoxic programs and can directly kill cancer cells [ 17 – 19 ]. Tissue-resident memory T cells can recognize a wide range of tumor antigens and upon reactivation they can rapidly clean tumor cells up [ 20 ]. Gamma-delta T cells possess the capability to destroy cancer cells through secreting cytokines and recruiting immune cells [ 21 ]. Given the critical role of T cells in immunity, it is necessary to further explore the gene expression profiles of T cells and their relationship with prognosis. The development of single-cell RNA sequencing (scRNA-seq) technology has provided researchers with a powerful tool to define TME subpopulations, reveal the molecular characteristics of different cell subpopulations, elucidate gene expression distribution information, dissect cellular transcriptomic heterogeneity and understand tumogenesis mechanisms [ 22 – 24 ]. Previous studies have demonstrated the feasibility and superiority of constructing prognostic models by exploring gene expression profiles of immune cells derived from scRNA-seq data [ 25 – 27 ]. In this study, we unraveled the transcriptomic landscape of CRC cell subtypes by analyzing scRNA-seq data. Then, we developed a novel prognostic signature based on T cell marker genes and evaluated its predictive performance in multiple CRC cohorts. The proposed signature provides a new insight that may pave the way for individualized management of CRC patients. Materials and methods Data resource ScRNA-seq data originating from tumor tissue samples and matched normal colorectal tissue samples from 10 patients with colorectal cancer, were obtained from the Gene Expression Omnibus (GEO) database, and the accession number was GSE132465. Bulk RNA sequencing data from four independent cohorts of CRC patients, including TCGA-CRC, GSE39582, GSE41258, and GSE17538, were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases, respectively. The sequencing data used in this study are publicly available for download (Table S1 ). Single-cell RNA sequencing data processing The single-cell sequencing raw data processed through the Cell Ranger (Version 7.1.0) pipeline were further subjected to analysis and visualization using the R software package Seurat (Version 4.3.0) workflow. In brief, we performed data quality control using the R software package Seurat, where we excluded cells and doublets of inadequate quality, as well as removed mitochondrial-related genes and hemoglobin-related genes. Following this, we applied data standardization, normalization, and performed principal component analysis (PCA). Additionally, we employed the R software package harmony (Version 0.1.1) to mitigate batch effects originating from different samples, enabling data reintegration and analysis. Using the K-nearest neighbors (KNN) algorithm, we performed unsupervised clustering of 20,439 cells, followed by gene-based cell annotation using widely accepted markers. We utilized the FindMarkers and FindAllMarkers functions for differential gene analysis to identify differentially expressed genes (DEGs) specific to each cell subpopulation. Furthermore, we extracted the single-cell gene expression matrix and clinical features from the Seurat object to be utilized in downstream analyses, including cell-cell communication and trajectory analysis. Functional enrichment analysis Functional enrichment analysis based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases was performed using the R software package clusterProfiler (Version 4.7.1). In brief, DEGs or highly expressed genes generated from the FindMarkers or FindAllMarkers functions in the Seurat package were input into clusterProfiler and run with default parameters. Enriched pathways with a significance level of P < 0.05 were selected for visualization. Gene set enrichment analysis (GSEA) was conducted using the clusterProfiler (Version 4.7.1) and msigdbr (Version 7.5.1) software packages. Target signaling pathways were obtained from the msigdbr package. DEGs with EntrezID were sorted based on log2Fold Change and input into the GSEA function, running with default parameters to obtain the normalized enrichment score (NES) for the target pathways. Copy number variation (CNV) analysis Copy number variation inference was conducted using the R software package infercnv (Version 1.14.2). Briefly, the single-cell gene expression matrix and clinical features extracted from Seurat were normalized and standardized. These data were then used as input for the infercnv::run function, running with default parameters, to calculate CNV scores in different epithelial cell subpopulations. T cells and monocytes were selected as the normal controls. Cell-cell communication analysis Analysis of cell-cell interactions is performed using the CellChat (Version 1.1.3) workflow in the R software package. In brief, the single-cell gene expression matrix and clinical features extracted from Seurat were input into CellChat. Data quality control, normalization, and feature selection were conducted to construct the CellChat object. Afterwards, unsupervised clustering was conducted using the k-means algorithm to calculate the relative distances between different cells. Based on the expression levels and patterns of general ligands and receptors, the communication quantity and strength between different cells were inferred. CellChat package and ggplot2 (Version 3.4.2) package were utilized for visualization. Cell trajectory analysis Cell trajectory analysis was performed using the R software packages CytoTRACE (Version 0.3.3) and monocle2 (Version 2.18.0). In brief, the single-cell gene expression matrix and clinical features extracted from Seurat were fed into the CytoTRACE function. The analysis was conducted using default parameters to calculate the CytoTRACE scores for different cell subgroups. Furthermore, the monocle2 software package was employed to infer the evolutionary trajectories of distinct cell subpopulations. Visualization was performed using the R software packages CytoTRACE, monocle2, and ggplot2. Construction and validation of prognostic model based on T cell-related genes Initially, we utilized the univariate Cox regression analysis to compute the risk genes significantly associated with prognosis in 4 independent CRC datasets (P < 0.05). Subsequently, we employed the FindAllMarkers function in the Seurat package to filter for the T cell-related gene set. Following cross-analysis, we identified SLC2A3, GADD45B, TERF2IP, SLC20A1, and MRPL22 as the candidate genes for model construction. Subsequently, based on the TCGA-CRC cohort, we constructed a 5 T cell-related genes prognostic prediction model using multivariable Cox regression analysis via R software package glmnet (Version 4.1-6). The prediction performance of the model was evaluated using ROC analysis. Furthermore, we included two independent CRC patient cohorts, GSE39582 and GSE41258, to assess the predictive performance of the model on additional datasets. Statistical Analysis The statistical analyses in this study were performed using R software (Version 3.6.1). The statistical data were presented as mean ± standard deviation. Student’s t-test was employed for assessing the significance between two groups, while one-way ANOVA analysis was performed for assessing the significance among multiple groups. A p-value of less than 0.05 was considered statistically significant in the data analysis. Results The single-cell expression atlas of CRC patients In order to investigate the single-cell sequencing atlas of CRC patients, we performed a reintegration and reanalysis of samples from 10 CRC patients and their paired healthy colorectal tissue samples obtained from GSE132465. After removing batch effects and performing quality control, we eliminated low-quality single cells, including dead cells and doublets, and finally obtained 20439 cells for subsequent analysis (Figure S1 A-F). Subsequently, we conducted PCA and KNN analyses, resulting in the identification of 23 distinct single-cell clusters from both normal colorectal and tumor tissues (Fig. 1 A-B). In order to investigate the association between different single-cell clusters, we employed Pearson correlation analysis. The results revealed a significant correlation in the gene expression patterns between clusters 1, 0, and 14, while clusters 11, 4, 10, 9, and 21 exhibited another similar gene expression pattern (Fig. 1 C). We further explored the marker genes of these single-cell clusters based on widely reported cell markers. The results revealed that clusters 0, 1, and 14 exhibited high expression of PTPRC, CD3D, and CD3E genes, thereby annotating as T cells. Conversely, clusters 4, 9, 10, 11, and 21 demonstrated high expression of EPCAM, KRT19, and CD24 genes, indicating their annotation as epithelial cells (Fig. 1 D-E). Further analysis of cell proportions revealed a significant increase in the proportion of epithelial cells and a relative decrease in plasma cells in tumor tissues compared to normal colorectal tissues (Fig. 1 F-G) [ 28 – 29 ]. This suggests a suppression of immune cell function in tumor tissues, which is consistent with existing reports. As the immune microenvironment plays a crucial role in tumor initiation and malignant progression, we focused on the distribution of immune cells that showed high expression of PTPRC, which is consistent with the previous cell annotation results (Fig. 1 H). In order to further supplement robust cell markers required in the cell annotation process, we investigated the highly expressed genes in different cell types. The results revealed that KRT18, KRT8, and FABP1 were highly expressed in epithelial cells, while CCL5 and GZMA were highly expressed in T cells, providing additional cell markers to the existing repertoire (Fig. 1 I). Epithelial cells were highly heterogeneous in CRC patients In order to investigate the differences in epithelial cells between normal and tumor groups, we performed tSNE dimensionality reduction and cell clustering on 5952 cells from single-cell sequencing data. The results showed that epithelial cells from 10 normal colorectal tissues and 10 CRC tissues exhibited high heterogeneity and could be clustered into 10 distinct cell subtypes (Fig. 2 A-B). Additionally, the results of the cell proportion analysis revealed that epithelial cells from clusters 4, 7, and 9 primarily originated from normal colorectal tissue, whereas epithelial cells from clusters 0, 1, 2, 3, 5, 6, and 8 mainly originated from distinct CRC tumor tissues, providing further validation of the heterogeneity of tumor cells (Fig. 2 C-D). Building upon the proven association between IFITM3 and immune suppression in CRC [ 30 ], we conducted a detailed examination of the expression levels of IFITM3 in single-cell RNA sequencing data. The findings revealed a significant upregulation of IFITM3 in malignant tumor cells (Fig. 2 E). The results from the copy number variation inference analysis yielded consistent findings, demonstrating a significantly lower CNV score in clusters 4, 7, and 9 compared to clusters 0, 1, 2, 3, 5, 6, and 8 (Fig. 2 F). We further examined the highly expressed genes in different subpopulations of epithelial cells. The results revealed a significant upregulation of genes related to benign development and immune activation, such as ZG16 and CCL5, in clusters 4, 7, and 9 [ 31 – 32 ]. Conversely, clusters 0, 1, and 2 exhibited a significant upregulation of genes associated with malignant proliferation and immune suppression, including APOA1BP, CXCL2, and CCL20 (Fig. 2 G) [ 33 – 34 ]. The results of GO and KEGG enrichment analysis showed a significant enrichment of ATP synthesis and transport-related pathways, as well as ribosome synthesis-related signaling pathways, in clusters 0 and 2 compared to clusters 4, 7, and 9. This indicates the activation of metabolic pathways in malignant epithelial cells (Figure S2A-B). Furthermore, we conducted differential expression analysis between normal colorectal tissue and CRC tissue. The results revealed a significant upregulation of genes such as DPEP1 and MMP7 in tumor tissue, consistent with the existing literature (Fig. 2 H) [ 35 – 36 ]. The GO enrichment analysis revealed a significant activation of pathways related to ribosome and mitochondrial function in epithelial cells derived from CRC tissue (Fig. 2 I). The heterogeneity of macrophages and plasma cells in CRC patients Considering the significantly increased macrophages and decreased plasma cells in CRC tissue compared to normal colorectal tissue (Fig. 1 F), we further analyzed the heterogeneity of macrophages and plasma cells in CRC. The results demonstrated that macrophages could be classified into 4 distinct clusters (Figure S3A), with cluster 1 mainly derived from normal colorectal tissue and clusters 0, 2, and 3 predominantly derived from CRC tissue (Figure S3B-C). The results of the differential gene expression analysis revealed that APOC1, STMN1, and IL32 were significantly upregulated in macrophages from clusters 0, 2, and 3 compared to cluster 1, and these genes have been proven to be associated with immune suppression in tumors (Figure S3D) [ 37 – 39 ]. The results of pathway enrichment analysis for the highly expressed genes revealed significant activation of pathways associated with cell proliferation and immune suppression in clusters 0, 2, and 3, including cell cycle and PD-1 checkpoint pathway (Figure S3E). Furthermore, plasma cells exhibited a high degree of heterogeneity and were categorized into 6 distinct clusters (Figure S4A). The results of cell proportion analysis showed that cluster 1 primarily originated from CRC tissue (Figure S4B-C). Differential gene expression analysis revealed high expression of IGHG1, IGHG4, and IGHG3 in cluster 1 plasma cells, consistent with existing reports (Figure S4D) [ 40 – 42 ]. Epithelial cells inhibited activation of T cells via MIF/CD74 signaling pathway To investigate intercellular interactions, we utilized the CellChat algorithm to infer changes in cell communication based on the gene expression levels of receptors and ligands (Fig. 3 A). The results indicated a significant increase in both the quantity and intensity of intercellular interactions in CRC tissue (Fig. 3 B). The results of differential analysis of intercellular interactions demonstrated a significant increase in the regulatory intensity of epithelial cells towards stromal cells in CRC tissue compared to normal colorectal tissue (Fig. 3 C). In terms of molecular mechanisms, CRC tissue shows a significant increase in relative information flow in pathways including SPP1, CCL, and MIF, while normal colorectal tissue exhibits enhanced relative information flow in pathways such as IL1 and PTN (Fig. 3 D). Furthermore, in comparison to normal colorectal tissue, a significant increase in the release of ligands, including MIF, SPP1, and CCL, by epithelial cells was observed in CRC tissue (Fig. 3 E). Correspondingly, there was a notable increase in the receptors for MIF, SPP1, and CCL in T cells and macrophages in CRC tissue (Fig. 3 F). Further differential analysis revealed that in CRC tissue, epithelial cells regulate the function of T cells through the MIF/CD74 pathway compared to the control group (Fig. 3 G-H). Immune-related pathways were inactivated in tumor-infiltrating T cells Given the significant role of T cells in tumor immunity in CRC, we conducted a further investigation on the functional alterations of T cells in CRC tissues. The results demonstrate that T cells derived from normal colorectal tissue and CRC tissue are classified into 6 clearly distinct clusters (Fig. 4 A). Based on universal cell markers, we annotated the 0 cluster as T helper cells due to their high expression of CCR6 and RORA; annotated the 1 cluster as NK T cells due to their high expression of NKG7 and ITGB2; annotated the 2 cluster as cytotoxic T cells due to their high expression of CD8A, CD8B, and CCL5; annotated the 3 cluster as exhausted T cells due to their high expression of LAG3 and CTSW; annotated the 4 cluster as Treg cells due to their high expression of CD4, TNFRSF4, and CORO1B; annotated the 5 cluster as naive T cells due to their high expression of CCR7 and SELL (Fig. 4 B-D). The analysis of cell proportions revealed a significant reduction in the proportion of cytotoxic T cells and a significant increase in the proportion of exhausted T cells in CRC tissues compared to normal colorectal tissue, consistent with previous findings (Fig. 4 E) [ 43 – 44 ]. The results of pathway enrichment analysis for highly expressed genes showed a significant enrichment of ATP-related pathways and a significant downregulation of immune-related pathways in exhausted T cells compared to cytotoxic T cells, indicating a dysregulation of both the metabolic pathways and immune function in exhausted T cells (Fig. 4 F-G). We conducted further analysis on the differential gene expression of cytotoxic T cells derived from CRC tissues and normal colorectal tissues. The results revealed a high expression of DUSP4, BATF, and TNFRSF18 in cytotoxic T cells derived from CRC tissues, all of which have been reported to be associated with malignant progression of tumors (Fig. 4 H) [ 45 – 47 ]. The results of pathway enrichment analysis further supported the significant activation of energy metabolism-related pathways and the significant inactivation of immune-related signaling pathways in cytotoxic T cells derived from CRC tissues (Fig. 4 I-G). Pseudotime and trajectory analyses revealed the different cell fates of T cells To investigate functional plasticity among T cell subpopulations, we analyzed the evolutionary trajectories of different T cell subpopulations via the CytoTRACE and monocle2 algorithms. The analysis results revealed that Treg cells and naive T cells exhibited higher CytoTRACE scores, indicating their strong cell proliferative capacity and differentiation potential (Fig. 5 A-B). The pseudotime analysis results based on monocle2 showed that naive T cells were located at the beginning of the trajectory, while Treg cells, exhausted T cells, and cytotoxic T cells were located at the endpoint, suggesting differentiation of T cells from naive T cells into distinct subpopulations (Fig. 5 C-D). Expression analysis of marker genes revealed that the expression levels of CCR7, SELL, and IL7R, which are highly expressed in naive T cells, were decreased during pseudotime. In contrast, the expression levels of CARD16, IFNG, and CXCL13, which are highly expressed in Treg cells, exhausted T cells, and cytotoxic T cells, were increased during pseudotime (Fig. 5 E). Gene regulatory transcription pattern analysis revealed that with increasing pseudotime, the expression level of gene cluster 1 was significantly decreased, while the expression levels of gene clusters 2, 3, and 4 were significantly increased (Fig. 5 F). The KEGG clustering analysis results indicated a significant enrichment of ribosome-related pathways in gene cluster 1, suggesting an active protein synthesis process in naive T cells. Genes in cluster 2 are significantly enriched in protein folding and cellular stress-related pathways, indicating the loss of immune-related functions in exhausted T cells. Gene cluster 3 is predominantly enriched in pathways associated with cell killing and NK cells, indicating the primary roles of cytotoxic T cells and NK cells in exerting cytotoxic effects. Gene cluster 4 is mainly enriched in pathways associated with antigen processing and presentation, indicating that Treg cells and T helper cells predominantly exert antigen presentation functions in CRC tissue (Fig. 5 G). Establishment and validation of 5 gene prognostic signature based on T cell-related markers To investigate the association between T cell-related genes and the survival time of CRC patients, we performed univariate Cox regression analysis to calculate the hazard-associated genes in 5 independent cohorts, followed by cross-analysis with T cell-related genes. The results indicated that SLC2A3, GADD45B, TERF2IP, SLC20A1, and MRPL22 were identified as risk genes in all 5 independent CRC cohorts (Fig. 6 A). Subsequently, we employed multivariable Cox regression analysis to construct a prognostic prediction model based on the 5 T cell-related risk genes in the TCGA cohort. The results indicated that the risk score could effectively distinguish the prognostic survival outcomes of CRC patients, with low-risk scores patients having a more favorable prognosis, while high-risk scores patients had a poorer prognosis (Fig. 6 B). The results of clinical characteristics and gene expression analysis revealed that with the patient’s risk score increasing, the mortality rate significantly increased, accompanied by increased expression levels of SLC2A3, GADD45B, TERF2IP, and SLC20A1, and decreased expression level of MRPL22 (Fig. 6 C). The results of the ROC analysis showed that the 5 T cell-related genes model had predictive AUCs of 0.60, 0.60, and 0.73 for 1 year, 2 years, and 3 years, respectively, indicating that the model exhibited good predictive performance (Fig. 6 D). We further included two independent CRC cohorts for validation of the model’s predictive performance. The results demonstrated that the 5 T cell-related genes model could significantly distinguish the survival outcomes of patients in the GSE39582 cohort and exhibited good predictive performance (Fig. 6 E-G). The validation results in the GSE41258 cohort also supported the same conclusion, confirming the robustness of the 5 T cell-related genes model in predicting CRC patient prognosis (Fig. 6 H-J). Discussion Currently, scRNA-seq is widely used to explore the molecular features of tumor-infiltrating immune cells in the TME and T cells have received significant attention in the prognosis of cancers [ 48 ]. In CRC, the abundance of tumor-infiltrating T cells is closely related to the prognosis of patients [ 49 – 50 ], indicating that it is a laudable attempt to construct a prognostic model based on T cell marker genes. Recently, Guo et al. comprehensively analyzed subpopulations, cell-cell interaction and trajectory of T cells in the TME and built a prognostic risk model using T cell marker genes to evaluate individual survival of patients with triple-negative breast cancer [ 51 ]. Inspired by this research, we aimed to identify the T cell marker genes with prognostic potential, and developed novel molecular classifiers based on these genes to guide future clinical management of CRC patients. In this study, we systematically investigated the heterogeneity and functional changes of various cells in the tumor microenvironment of CRC patients through the reintegration and reanalysis of single-cell datasets. In particular, we investigated the alterations in cell-cell communications within normal colon and CRC tissues, and revealed the functional shift in tumor-infiltrating T cells induced by tumor cells via the MIF/CD74 signaling pathway. While additional confirmation is required, the inhibitory effects of the MIF/CD74 signaling pathway on T cells have been extensively documented in various types of tumors [ 52 ]. Our findings are consistent with existing reports and highlight the oncogenic role of the MIF/CD74 signaling pathway in CRC. Furthermore, through pseudo-time analysis, we investigated the evolutionary trajectory of T cells and revealed the molecular alterations underlying the transition from naive T cells to exhausted T cells in CRC tissues. These findings provide a novel theoretical basis for immunotherapy and targeted therapy in CRC. Eventually, we integrated single-cell and bulk RNA sequencing analysis to construct a prognostic prediction model based on T cell-related genes via multivariable Cox regression algorithm. The validation results from several independent CRC cohorts indicated that the 5 T cell-related genes prognostic model could accurately predict the survival outcomes of CRC patients, providing new evidence for precision treatment in CRC. Among the five genes, GADD45B, SLC2A3 and TERF2IP were previously reported to be tightly associated with CRC. GADD45B is a member of the growth arrest and DNA damage-inducible gene family, involved in cell growth control, DNA damage response and apoptosis [ 53 ]. GADD45B is upregulated in CRC tissues, and its high expression is indicative of short overall survival and disease-free survival [ 54 – 55 ]. SLC2A3 is a member of the solute carrier family whose function is to transport glucose [ 56 ]. SLC2A3 is highly expressed in CRC and negatively correlated with the prognosis of CRC patients. SLC2A3 promotes invasiveness and cancer stem cell-like properties of CRC cells through forming a positive regulatory loop with the Hippo cascade transducer Yes-associated protein [ 57 – 58 ]. Moreover, SLC2A3 fosters the growth of CRC cells by expediting glucose input, upregulating aerobic glycolysis and facilitating nucleotide synthesis [ 59 – 60 ]. TERF2IP is a crucial component of shelterin and it bolsters CRC cell invasion by activating the MAPK signaling pathway [ 61 ]. Although SLC20A1 has not been reported in CRC, its prognostic value in breast cancer, esophageal adenocarcinoma and pancreatic cancer has been demonstrated [ 62 – 64 ]. The MRPL22 gene has not been reported in cancer. Despite the promising findings obtained, our research has some limitations. Firstly, the study was retrospective, with transcriptome data downloaded from publicly available GEO databases, thus the current findings need to be validated in prospective and real-word studies. Secondly, predictive performance of the 5-gene signature at the protein level deserves further investigation. In addition, the biological functions and potential mechanisms of SLC20A1 and MRPL22 in CRC remain unclear. Future efforts should focus on verifying the efficacy of the model through in-house cohorts, exploring the molecular mechanisms of SLC20A1 and MRPL22, and providing experimental basis for the clinical application of our prognostic model. In conclusion, we analyzed scRNA-seq data of CRC tissues and proposed a novel prognostic signature based on T-cell marker genes. This signature exhibited moderate predictive performance and might help to guide clinical decision. Our study provides a novel insight into the role of immune cell marker genes in cancer prognosis. Abbreviations CNV = Copy number variation; CRC = colorectal cancer; DEG = differentially expressed genes; GEO = Gene Expression Omnibus; GO = gene ontology; GSEA = Gene set enrichment analysis; KEGG = Kyoto Encyclopedia of Genes and Genomes; K-M = Kaplan–Meier; KNN = K-nearest neighbors; NES = normalized enrichment score; PCA = principal component analysis; ROC = receiver operating characteristic; ScRNA-seq = Single-cell RNA-sequencing; TCGA = The Cancer Genome Atlas; TME = tumor microenvironment. Declarations Ethics approval and consent to participate The study was conducted with the approval of the Ethics Committee of Shanghai Changhai Hospital Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding: National Natural Science Foundation of China(82203137, 82072750, 82272705), Shanghai Sailing Program(21YF1459300), 71st Batch of China Postdoctoral Science Foundation (48804), Three-year action plan to promote clinical skills and clinical innovation in municipal hospitals(SHDC2022CRT007) Authors’ Contributions: Wei Zhang, Guanyu Yu and Fuao Cao designed the research. Xiaoming Zhu, Rongbo Wen, Jiaqi Wu and Leqi Zhou made substantial contributions to acquisition, analysis and interpretation of data, and wrote the manuscript. Hao Fan, Tianshuai Zhang, Yiyang Li and Zixuan Liu coordinated and were involved in acquisition, interpretation of the data. Wei Zhang, Guanyu Yu and Fuao Cao revised it critically for important intellectual content and gave final approval of the version to be published. Acknowledgements Not applicable. Availability of data and materials The data supporting this study’s findings are available on request from the corresponding author Data Availability Statement All datasets presented in this study are included in the article/supplementary material. Conflict of Interest Authors declare that they have no competing interests. Acknowledgements We thank The National Natural Science Foundation of China for the grant funding. We acknowledge the contributions from the GEO and TCGA databases. References Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A: Colorectal cancer statistics, 2023. CA: a cancer journal for clinicians 2023, 73(3):233–254. Kuipers EJ, Grady WM, Lieberman D, Seufferlein T, Sung JJ, Boelens PG, van de Velde CJ, Watanabe T: Colorectal cancer. Nature reviews Disease primers 2015, 1:15065. 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Ha TK, Her NG, Lee MG, Ryu BK, Lee JH, Han J, Jeong SI, Kang MJ, Kim NH, Kim HJ et al : Caveolin-1 increases aerobic glycolysis in colorectal cancers by stimulating HMGA1-mediated GLUT3 transcription. Cancer Res 2012, 72(16):4097–4109. House CD, Wang BD, Ceniccola K, Williams R, Simaan M, Olender J, Patel V, Baptista-Hon DT, Annunziata CM, Gutkind JS et al : Voltage-gated Na + Channel Activity Increases Colon Cancer Transcriptional Activity and Invasion Via Persistent MAPK Signaling. Scientific reports 2015, 5:11541. Sato K, Akimoto K: Expression Levels of KMT2C and SLC20A1 Identified by Information-theoretical Analysis Are Powerful Prognostic Biomarkers in Estrogen Receptor-positive Breast Cancer. Clinical breast cancer 2017, 17(3):e135-e142. Dong Z, Wang J, Zhan T, Xu S: Identification of prognostic risk factors for esophageal adenocarcinoma using bioinformatics analysis. OncoTargets and therapy 2018, 11:4327–4337. Haider S, Wang J, Nagano A, Desai A, Arumugam P, Dumartin L, Fitzgibbon J, Hagemann T, Marshall JF, Kocher HM et al : A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma. Genome medicine 2014, 6(12):105. Additional Declarations (Not answered) Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3909225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270110016,"identity":"313abf95-a389-4156-bd57-481c93178eef","order_by":0,"name":"Wei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie2Rv2rDMBCHJQTucsarRfsQgoBLIdivYiPo5KEP0OGCQJnS2X2R0lFC4CymXQ0ptKHQOSFQyFL6J5TSIXLGQvUtd8Pv4+44QgKBPwlF9lkSRtEQMoYkwQMVPlX2iZDzE96Y4TlfiuhaKQhxY4GlPy7mSm0uLh9y7Oss3d7egyCGrta1R+ksjpr2RU6aOuOzbgGnDBm/vtmvZH2FEiInWVpnx1Qv4AxNxGKf8rhEB29ORjvlDoQpB5SeTlSsXQ7QyhHVZlgpukqx+MqV6ZGyy5mWwBurvLfw6fx5A6+uKNzHK7c6L5JE2dXao3xT4U9PcV/q94IHpQKBQOB/8g4tu1jP9NiXBwAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":270110017,"identity":"83fab7a2-0789-476c-942b-8880ef24acc8","order_by":1,"name":"Xiaoming Zhu","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Zhu","suffix":""},{"id":270110018,"identity":"da78e6cd-c20d-445e-9316-b8bdebd81025","order_by":2,"name":"Rongbo Wen","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rongbo","middleName":"","lastName":"Wen","suffix":""},{"id":270110019,"identity":"29d326f9-39f9-4c6c-967e-1a4f5a70ef1b","order_by":3,"name":"Jiaqi Wu","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Wu","suffix":""},{"id":270110020,"identity":"d2a52d8b-15e7-4d22-b4b1-af1153e8b419","order_by":4,"name":"Leqi Zhou","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leqi","middleName":"","lastName":"Zhou","suffix":""},{"id":270110021,"identity":"9be1c4d4-138e-4d12-8f8d-68c6243c14ea","order_by":5,"name":"Hao Fan","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Fan","suffix":""},{"id":270110022,"identity":"9f1a0abc-b984-421d-9437-70e4fea094c5","order_by":6,"name":"Tianshuai zhang","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianshuai","middleName":"","lastName":"zhang","suffix":""},{"id":270110023,"identity":"90faf96f-ecba-462d-839a-a9772af498e8","order_by":7,"name":"Yiyang Li","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Li","suffix":""},{"id":270110024,"identity":"8eebb09a-039f-49e2-8c13-7ab116e65ac3","order_by":8,"name":"Zixuan Liu","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Liu","suffix":""},{"id":270110025,"identity":"db25222c-97cd-4a1d-83b0-03c8d4ddbe95","order_by":9,"name":"Guanyu Yu","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guanyu","middleName":"","lastName":"Yu","suffix":""},{"id":270110026,"identity":"80c67183-873e-4488-ab8e-3bf1d3cc581c","order_by":10,"name":"Fuao Cao","email":"","orcid":"","institution":"Shanghai Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fuao","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-01-29 16:27:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50521507,"identity":"cbb6da80-62b6-4e5f-bdd9-8318e40fe218","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1627754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe single-cell expression atlas of CRC patients. \u003c/strong\u003e(A) tSNE plot showing the 23 clusters of scRNA-seq data from CRC patients. (B) tSNE plots representing single-cell data from different groups. (C) Heat map showing the correlation between 23 clusters of scRNA-seq data from CRC patients. (D) Dot plot showing the gene expression level of 23 clusters based on well-known marker genes. (E) tSNE plot representing different cell types of scRNA-seq data from CRC patients. (F) Bar plot showing the proportion of different cell types in normal and tumor groups. (G) tSNE plot showing different cell types of scRNA-seq data from normal or tumor groups. (H) tSNE plot showing the expression level of PTPRC of scRNA-seq data from CRC patients. (I) Volcano plot showing the highly expressed genes in different cell types.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/82f6c2467c7347cef2706211.png"},{"id":50521509,"identity":"52075299-a79b-4e0a-8317-0d42f20a7580","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3077226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpithelial cells were highly heterogeneous in CRC patients. \u003c/strong\u003e(A) tSNE plot displaying distinct subpopulations of epithelial cells. (B) tSNE plot showing epithelial cells derived from different tissues. (C) tSNE plot showing epithelial cells from normal colon tissue and CRC tissue. (D) Bar plot illustrating the proportions of epithelial cells in normal colon tissue and CRC tissue. (E) tSNE plot displaying the expression levels of the IFITM3 gene. (F) Heatmap depicting copy number variation scores across different subpopulations of epithelial cells. (G) Volcano plot illustrating highly expressed genes across different subpopulations of epithelial cells. (H) Volcano plot depicting differentially expressed genes in epithelial cells derived from normal tissue and CRC tissue. (I) Chord diagram depicting GO enrichment analysis of differentially expressed genes in epithelial cells derived from normal tissue and CRC tissue.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/7c02766edb7c63104b734b95.png"},{"id":50521503,"identity":"49b7a97f-b5bb-4bdf-9811-15a88752dcb3","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1134902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpithelial cells inhibited activation of T cells via MIF/CD74 signaling pathway. \u003c/strong\u003e(A) Circular diagram illustrating the interactions between various cell types in CRC tissue. (B) Bar graph illustrating the quantity and strength of cell-cell interactions. (C) Heatmap depicting the differential strength of cell-cell interactions between CRC tissue and normal colon tissue. (D) Bar plot depicting the information flow of various signaling pathways between CRC tissue and normal colon tissue. (E) Heatmap illustrating the strength of outgoing signaling patterns from different cell types. (F) Heatmap illustrating the strength of incoming signaling patterns from different cell types. (G) Dot plot illustrating the strength of regulatory pathways exerted by epithelial cells on various immune cells. (H) Heatmap illustrating the strength of the MIF pathway in normal colon tissue and CRC tissue.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/a9dbba734cdff1d75673a5d3.png"},{"id":50521504,"identity":"f41d50dc-ea68-40f4-9989-7f65182b2fe9","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1490201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune-related pathways were inactivated in tumor-infiltrating T cells. \u003c/strong\u003e(A) tSNE plot displaying distinct subpopulations of T cells based on KNN algorithm. (B) Dot plot showing the gene expression level of 6 clusters based on well-known marker genes. (C) tSNE plot displaying distinct subpopulations of T cells. (D) Heatmap showing the highly expressed genes in different subpopulations of T cells. (E) Bar plot and tSNE plot illustrating the proportions of T cells in normal colon tissue and CRC tissue. (F) Dot plot showing the KEGG enrichment analysis results for highly expressed genes in different subpopulations of T cells. (G) Violin plot showing the score of immune response activation in different subpopulations of T cells. (H) Volcano plot depicting differentially expressed genes in cytotoxic T cells derived from normal tissue and CRC tissue. (I) Bar plot showing the KEGG enrichment analysis results for upregulated genes in cytotoxic T cells derived from CRC tissue. (G) GSEA showing the activation of several pathways based on DEGs between cytotoxic T cells derived from CRC tissue and normal colon tissue.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/20ed75f81e6343c8b49121eb.png"},{"id":50521506,"identity":"d8003b6d-0016-43bc-a1db-287fee63e60c","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1441982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePseudotime and trajectory analyses revealed the different cell fates of T cells.\u003c/strong\u003e (A) tSNE plot depicting the CytoTRACE scores of distinct subpopulations of T cells. (B) Boxplot displaying the CytoTRACE scores of different subpopulations of T cells. (C) PCA plot illustrating the pseudotime distribution within T cell subpopulations. (D) PCA plot illustrating the distribution of distinct T cell subpopulations. (E) Scatter plot depicting the expression changes of CCR7, CARD16, IFNG, CXCL13, SELL, and IL7R genes. (F) Heatmap displaying the expression changes of gene clusters across pseudotime. (G) Bar plot illustrating the KEGG enrichment results for distinct gene clusters.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/d2daadb87030034490d8c035.png"},{"id":50521975,"identity":"c026c5f3-7449-4ec0-b277-fc33847aaeb3","added_by":"auto","created_at":"2024-02-01 19:19:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":950776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment and validation of 5 gene prognostic signature based on T cell-related markers. \u003c/strong\u003e(A) Venn diagrams illustrating the intersection analysis between T cell-related genes and risk genes in 5 independent CRC cohorts. (B) Kaplan-Meier curves depicting overall survival of high, medium, and low-risk patients in the TCGA training cohort. (C) Scatter plots and heatmaps illustrating the correlation between risk scores, patient survival, and gene expression patterns in the TCGA training cohort. (D) ROC curve depicting the predictive performance of the 5 T cell-related genes model in the TCGA training cohort. (E) Kaplan-Meier curves depicting overall survival of high, medium, and low-risk patients in the GSE39582 validation cohort. (F) Scatter plots and heatmaps illustrating the correlation between risk scores, patient survival, and gene expression patterns in the GSE39582 validation cohort. (G) ROC curve depicting the predictive performance of the 5 T cell-related genes model in the GSE39582 validation cohort. (H) Kaplan-Meier curves depicting overall survival of high, medium, and low-risk patients in the GSE41258 validation cohort. (I) Scatter plots and heatmaps illustrating the correlation between risk scores, patient survival, and gene expression patterns in the GSE41258 validation cohort. (J) ROC curve depicting the predictive performance of the 5 T cell-related genes model in the GSE41258 validation cohort.\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/e87846099acb22c2c03d7c1c.png"},{"id":51053121,"identity":"5e046ede-c1cc-40b7-9b28-e3733a69ca62","added_by":"auto","created_at":"2024-02-13 11:04:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4166878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/4cbf0333-90c6-4eee-8a8a-7282a9a1c9cf.pdf"},{"id":50521508,"identity":"a94c96e0-4a9f-4c05-8133-30dd9713ecee","added_by":"auto","created_at":"2024-02-01 19:11:09","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2082688,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909225/v1/b42f1cb9496bc5e634f8fbe2.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is a common and deadly malignancy of the digestive system, with an estimated 153,020 new cases and 52,550 deaths in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite marked advances in screening programs and medical therapies, survival rates of CRC have barely increased over the past several decades [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The lack of effective prognostic indicators to guide personalized treatment has greatly hindered the significant improvement of patient outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the high biological heterogeneity and complex pathogenesis of CRC, existing prognostic predictors are insufficient to capture individual survival differences [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this context, there is an urgent need to develop more accurate molecular classifiers to assess prognosis.\u003c/p\u003e \u003cp\u003ePrevious prognostic models mainly focus on cell-autonomous changes of CRC, based on biological processes occurred in cancer cells such as senescence, metabolism and cuproptosis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and few studies focus on the tumor microenvironment (TME). In solid cancer, cancerous cells are surrounded by the TME, an important and complex structure that comprises stromal cells, diversity of immune cells, fibroblasts and various cytokines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Great attention has been drawn on recognizing the crucial role of TME in tumor initiation and progression. Accumulating evidence indicates that abnormal changes in the immune component of TME can not only regulate cancer progression but also affect patient survival, making the immune microenvironment an under-explored source of prognostic markers [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among immune cells, T cells are the key mediators of anti-tumor immunity, and T cell-based immunotherapies have emerged as new therapeutic pillars within oncology [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Pre-clinical and clinical studies have confirmed that CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells possess cytotoxic programs and can directly kill cancer cells [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Tissue-resident memory T cells can recognize a wide range of tumor antigens and upon reactivation they can rapidly clean tumor cells up [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gamma-delta T cells possess the capability to destroy cancer cells through secreting cytokines and recruiting immune cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given the critical role of T cells in immunity, it is necessary to further explore the gene expression profiles of T cells and their relationship with prognosis.\u003c/p\u003e \u003cp\u003eThe development of single-cell RNA sequencing (scRNA-seq) technology has provided researchers with a powerful tool to define TME subpopulations, reveal the molecular characteristics of different cell subpopulations, elucidate gene expression distribution information, dissect cellular transcriptomic heterogeneity and understand tumogenesis mechanisms [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies have demonstrated the feasibility and superiority of constructing prognostic models by exploring gene expression profiles of immune cells derived from scRNA-seq data [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, we unraveled the transcriptomic landscape of CRC cell subtypes by analyzing scRNA-seq data. Then, we developed a novel prognostic signature based on T cell marker genes and evaluated its predictive performance in multiple CRC cohorts. The proposed signature provides a new insight that may pave the way for individualized management of CRC patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData resource\u003c/h2\u003e \u003cp\u003eScRNA-seq data originating from tumor tissue samples and matched normal colorectal tissue samples from 10 patients with colorectal cancer, were obtained from the Gene Expression Omnibus (GEO) database, and the accession number was GSE132465. Bulk RNA sequencing data from four independent cohorts of CRC patients, including TCGA-CRC, GSE39582, GSE41258, and GSE17538, were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases, respectively. The sequencing data used in this study are publicly available for download (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA sequencing data processing\u003c/h2\u003e \u003cp\u003eThe single-cell sequencing raw data processed through the Cell Ranger (Version 7.1.0) pipeline were further subjected to analysis and visualization using the R software package Seurat (Version 4.3.0) workflow. In brief, we performed data quality control using the R software package Seurat, where we excluded cells and doublets of inadequate quality, as well as removed mitochondrial-related genes and hemoglobin-related genes. Following this, we applied data standardization, normalization, and performed principal component analysis (PCA). Additionally, we employed the R software package harmony (Version 0.1.1) to mitigate batch effects originating from different samples, enabling data reintegration and analysis. Using the K-nearest neighbors (KNN) algorithm, we performed unsupervised clustering of 20,439 cells, followed by gene-based cell annotation using widely accepted markers. We utilized the FindMarkers and FindAllMarkers functions for differential gene analysis to identify differentially expressed genes (DEGs) specific to each cell subpopulation. Furthermore, we extracted the single-cell gene expression matrix and clinical features from the Seurat object to be utilized in downstream analyses, including cell-cell communication and trajectory analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases was performed using the R software package clusterProfiler (Version 4.7.1). In brief, DEGs or highly expressed genes generated from the FindMarkers or FindAllMarkers functions in the Seurat package were input into clusterProfiler and run with default parameters. Enriched pathways with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for visualization. Gene set enrichment analysis (GSEA) was conducted using the clusterProfiler (Version 4.7.1) and msigdbr (Version 7.5.1) software packages. Target signaling pathways were obtained from the msigdbr package. DEGs with EntrezID were sorted based on log2Fold Change and input into the GSEA function, running with default parameters to obtain the normalized enrichment score (NES) for the target pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCopy number variation (CNV) analysis\u003c/h2\u003e \u003cp\u003eCopy number variation inference was conducted using the R software package infercnv (Version 1.14.2). Briefly, the single-cell gene expression matrix and clinical features extracted from Seurat were normalized and standardized. These data were then used as input for the infercnv::run function, running with default parameters, to calculate CNV scores in different epithelial cell subpopulations. T cells and monocytes were selected as the normal controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell communication analysis\u003c/h2\u003e \u003cp\u003eAnalysis of cell-cell interactions is performed using the CellChat (Version 1.1.3) workflow in the R software package. In brief, the single-cell gene expression matrix and clinical features extracted from Seurat were input into CellChat. Data quality control, normalization, and feature selection were conducted to construct the CellChat object. Afterwards, unsupervised clustering was conducted using the k-means algorithm to calculate the relative distances between different cells. Based on the expression levels and patterns of general ligands and receptors, the communication quantity and strength between different cells were inferred. CellChat package and ggplot2 (Version 3.4.2) package were utilized for visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell trajectory analysis\u003c/h2\u003e \u003cp\u003eCell trajectory analysis was performed using the R software packages CytoTRACE (Version 0.3.3) and monocle2 (Version 2.18.0). In brief, the single-cell gene expression matrix and clinical features extracted from Seurat were fed into the CytoTRACE function. The analysis was conducted using default parameters to calculate the CytoTRACE scores for different cell subgroups. Furthermore, the monocle2 software package was employed to infer the evolutionary trajectories of distinct cell subpopulations. Visualization was performed using the R software packages CytoTRACE, monocle2, and ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of prognostic model based on T cell-related genes\u003c/h2\u003e \u003cp\u003eInitially, we utilized the univariate Cox regression analysis to compute the risk genes significantly associated with prognosis in 4 independent CRC datasets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, we employed the FindAllMarkers function in the Seurat package to filter for the T cell-related gene set. Following cross-analysis, we identified SLC2A3, GADD45B, TERF2IP, SLC20A1, and MRPL22 as the candidate genes for model construction. Subsequently, based on the TCGA-CRC cohort, we constructed a 5 T cell-related genes prognostic prediction model using multivariable Cox regression analysis via R software package glmnet (Version 4.1-6). The prediction performance of the model was evaluated using ROC analysis. Furthermore, we included two independent CRC patient cohorts, GSE39582 and GSE41258, to assess the predictive performance of the model on additional datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses in this study were performed using R software (Version 3.6.1). The statistical data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Student\u0026rsquo;s t-test was employed for assessing the significance between two groups, while one-way ANOVA analysis was performed for assessing the significance among multiple groups. A p-value of less than 0.05 was considered statistically significant in the data analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe single-cell expression atlas of CRC patients\u003c/h2\u003e \u003cp\u003eIn order to investigate the single-cell sequencing atlas of CRC patients, we performed a reintegration and reanalysis of samples from 10 CRC patients and their paired healthy colorectal tissue samples obtained from GSE132465. After removing batch effects and performing quality control, we eliminated low-quality single cells, including dead cells and doublets, and finally obtained 20439 cells for subsequent analysis (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-F). Subsequently, we conducted PCA and KNN analyses, resulting in the identification of 23 distinct single-cell clusters from both normal colorectal and tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). In order to investigate the association between different single-cell clusters, we employed Pearson correlation analysis. The results revealed a significant correlation in the gene expression patterns between clusters 1, 0, and 14, while clusters 11, 4, 10, 9, and 21 exhibited another similar gene expression pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We further explored the marker genes of these single-cell clusters based on widely reported cell markers. The results revealed that clusters 0, 1, and 14 exhibited high expression of PTPRC, CD3D, and CD3E genes, thereby annotating as T cells. Conversely, clusters 4, 9, 10, 11, and 21 demonstrated high expression of EPCAM, KRT19, and CD24 genes, indicating their annotation as epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). Further analysis of cell proportions revealed a significant increase in the proportion of epithelial cells and a relative decrease in plasma cells in tumor tissues compared to normal colorectal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This suggests a suppression of immune cell function in tumor tissues, which is consistent with existing reports. As the immune microenvironment plays a crucial role in tumor initiation and malignant progression, we focused on the distribution of immune cells that showed high expression of PTPRC, which is consistent with the previous cell annotation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). In order to further supplement robust cell markers required in the cell annotation process, we investigated the highly expressed genes in different cell types. The results revealed that KRT18, KRT8, and FABP1 were highly expressed in epithelial cells, while CCL5 and GZMA were highly expressed in T cells, providing additional cell markers to the existing repertoire (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEpithelial cells were highly heterogeneous in CRC patients\u003c/h2\u003e \u003cp\u003eIn order to investigate the differences in epithelial cells between normal and tumor groups, we performed tSNE dimensionality reduction and cell clustering on 5952 cells from single-cell sequencing data. The results showed that epithelial cells from 10 normal colorectal tissues and 10 CRC tissues exhibited high heterogeneity and could be clustered into 10 distinct cell subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Additionally, the results of the cell proportion analysis revealed that epithelial cells from clusters 4, 7, and 9 primarily originated from normal colorectal tissue, whereas epithelial cells from clusters 0, 1, 2, 3, 5, 6, and 8 mainly originated from distinct CRC tumor tissues, providing further validation of the heterogeneity of tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Building upon the proven association between IFITM3 and immune suppression in CRC [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we conducted a detailed examination of the expression levels of IFITM3 in single-cell RNA sequencing data. The findings revealed a significant upregulation of IFITM3 in malignant tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The results from the copy number variation inference analysis yielded consistent findings, demonstrating a significantly lower CNV score in clusters 4, 7, and 9 compared to clusters 0, 1, 2, 3, 5, 6, and 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We further examined the highly expressed genes in different subpopulations of epithelial cells. The results revealed a significant upregulation of genes related to benign development and immune activation, such as ZG16 and CCL5, in clusters 4, 7, and 9 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, clusters 0, 1, and 2 exhibited a significant upregulation of genes associated with malignant proliferation and immune suppression, including APOA1BP, CXCL2, and CCL20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The results of GO and KEGG enrichment analysis showed a significant enrichment of ATP synthesis and transport-related pathways, as well as ribosome synthesis-related signaling pathways, in clusters 0 and 2 compared to clusters 4, 7, and 9. This indicates the activation of metabolic pathways in malignant epithelial cells (Figure S2A-B). Furthermore, we conducted differential expression analysis between normal colorectal tissue and CRC tissue. The results revealed a significant upregulation of genes such as DPEP1 and MMP7 in tumor tissue, consistent with the existing literature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The GO enrichment analysis revealed a significant activation of pathways related to ribosome and mitochondrial function in epithelial cells derived from CRC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe heterogeneity of macrophages and plasma cells in CRC patients\u003c/h2\u003e \u003cp\u003eConsidering the significantly increased macrophages and decreased plasma cells in CRC tissue compared to normal colorectal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), we further analyzed the heterogeneity of macrophages and plasma cells in CRC. The results demonstrated that macrophages could be classified into 4 distinct clusters (Figure S3A), with cluster 1 mainly derived from normal colorectal tissue and clusters 0, 2, and 3 predominantly derived from CRC tissue (Figure S3B-C). The results of the differential gene expression analysis revealed that APOC1, STMN1, and IL32 were significantly upregulated in macrophages from clusters 0, 2, and 3 compared to cluster 1, and these genes have been proven to be associated with immune suppression in tumors (Figure S3D) [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The results of pathway enrichment analysis for the highly expressed genes revealed significant activation of pathways associated with cell proliferation and immune suppression in clusters 0, 2, and 3, including cell cycle and PD-1 checkpoint pathway (Figure S3E). Furthermore, plasma cells exhibited a high degree of heterogeneity and were categorized into 6 distinct clusters (Figure S4A). The results of cell proportion analysis showed that cluster 1 primarily originated from CRC tissue (Figure S4B-C). Differential gene expression analysis revealed high expression of IGHG1, IGHG4, and IGHG3 in cluster 1 plasma cells, consistent with existing reports (Figure S4D) [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEpithelial cells inhibited activation of T cells via MIF/CD74 signaling pathway\u003c/h2\u003e \u003cp\u003eTo investigate intercellular interactions, we utilized the CellChat algorithm to infer changes in cell communication based on the gene expression levels of receptors and ligands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The results indicated a significant increase in both the quantity and intensity of intercellular interactions in CRC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results of differential analysis of intercellular interactions demonstrated a significant increase in the regulatory intensity of epithelial cells towards stromal cells in CRC tissue compared to normal colorectal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In terms of molecular mechanisms, CRC tissue shows a significant increase in relative information flow in pathways including SPP1, CCL, and MIF, while normal colorectal tissue exhibits enhanced relative information flow in pathways such as IL1 and PTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, in comparison to normal colorectal tissue, a significant increase in the release of ligands, including MIF, SPP1, and CCL, by epithelial cells was observed in CRC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Correspondingly, there was a notable increase in the receptors for MIF, SPP1, and CCL in T cells and macrophages in CRC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Further differential analysis revealed that in CRC tissue, epithelial cells regulate the function of T cells through the MIF/CD74 pathway compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmune-related pathways were inactivated in tumor-infiltrating T cells\u003c/h2\u003e \u003cp\u003eGiven the significant role of T cells in tumor immunity in CRC, we conducted a further investigation on the functional alterations of T cells in CRC tissues. The results demonstrate that T cells derived from normal colorectal tissue and CRC tissue are classified into 6 clearly distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Based on universal cell markers, we annotated the 0 cluster as T helper cells due to their high expression of CCR6 and RORA; annotated the 1 cluster as NK T cells due to their high expression of NKG7 and ITGB2; annotated the 2 cluster as cytotoxic T cells due to their high expression of CD8A, CD8B, and CCL5; annotated the 3 cluster as exhausted T cells due to their high expression of LAG3 and CTSW; annotated the 4 cluster as Treg cells due to their high expression of CD4, TNFRSF4, and CORO1B; annotated the 5 cluster as naive T cells due to their high expression of CCR7 and SELL (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D). The analysis of cell proportions revealed a significant reduction in the proportion of cytotoxic T cells and a significant increase in the proportion of exhausted T cells in CRC tissues compared to normal colorectal tissue, consistent with previous findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The results of pathway enrichment analysis for highly expressed genes showed a significant enrichment of ATP-related pathways and a significant downregulation of immune-related pathways in exhausted T cells compared to cytotoxic T cells, indicating a dysregulation of both the metabolic pathways and immune function in exhausted T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G). We conducted further analysis on the differential gene expression of cytotoxic T cells derived from CRC tissues and normal colorectal tissues. The results revealed a high expression of DUSP4, BATF, and TNFRSF18 in cytotoxic T cells derived from CRC tissues, all of which have been reported to be associated with malignant progression of tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH) [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The results of pathway enrichment analysis further supported the significant activation of energy metabolism-related pathways and the significant inactivation of immune-related signaling pathways in cytotoxic T cells derived from CRC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime and trajectory analyses revealed the different cell fates of T cells\u003c/h2\u003e \u003cp\u003eTo investigate functional plasticity among T cell subpopulations, we analyzed the evolutionary trajectories of different T cell subpopulations via the CytoTRACE and monocle2 algorithms. The analysis results revealed that Treg cells and naive T cells exhibited higher CytoTRACE scores, indicating their strong cell proliferative capacity and differentiation potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). The pseudotime analysis results based on monocle2 showed that naive T cells were located at the beginning of the trajectory, while Treg cells, exhausted T cells, and cytotoxic T cells were located at the endpoint, suggesting differentiation of T cells from naive T cells into distinct subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Expression analysis of marker genes revealed that the expression levels of CCR7, SELL, and IL7R, which are highly expressed in naive T cells, were decreased during pseudotime. In contrast, the expression levels of CARD16, IFNG, and CXCL13, which are highly expressed in Treg cells, exhausted T cells, and cytotoxic T cells, were increased during pseudotime (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Gene regulatory transcription pattern analysis revealed that with increasing pseudotime, the expression level of gene cluster 1 was significantly decreased, while the expression levels of gene clusters 2, 3, and 4 were significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The KEGG clustering analysis results indicated a significant enrichment of ribosome-related pathways in gene cluster 1, suggesting an active protein synthesis process in naive T cells. Genes in cluster 2 are significantly enriched in protein folding and cellular stress-related pathways, indicating the loss of immune-related functions in exhausted T cells. Gene cluster 3 is predominantly enriched in pathways associated with cell killing and NK cells, indicating the primary roles of cytotoxic T cells and NK cells in exerting cytotoxic effects. Gene cluster 4 is mainly enriched in pathways associated with antigen processing and presentation, indicating that Treg cells and T helper cells predominantly exert antigen presentation functions in CRC tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and validation of 5 gene prognostic signature based on T cell-related markers\u003c/h2\u003e \u003cp\u003eTo investigate the association between T cell-related genes and the survival time of CRC patients, we performed univariate Cox regression analysis to calculate the hazard-associated genes in 5 independent cohorts, followed by cross-analysis with T cell-related genes. The results indicated that SLC2A3, GADD45B, TERF2IP, SLC20A1, and MRPL22 were identified as risk genes in all 5 independent CRC cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, we employed multivariable Cox regression analysis to construct a prognostic prediction model based on the 5 T cell-related risk genes in the TCGA cohort. The results indicated that the risk score could effectively distinguish the prognostic survival outcomes of CRC patients, with low-risk scores patients having a more favorable prognosis, while high-risk scores patients had a poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The results of clinical characteristics and gene expression analysis revealed that with the patient\u0026rsquo;s risk score increasing, the mortality rate significantly increased, accompanied by increased expression levels of SLC2A3, GADD45B, TERF2IP, and SLC20A1, and decreased expression level of MRPL22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The results of the ROC analysis showed that the 5 T cell-related genes model had predictive AUCs of 0.60, 0.60, and 0.73 for 1 year, 2 years, and 3 years, respectively, indicating that the model exhibited good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). We further included two independent CRC cohorts for validation of the model\u0026rsquo;s predictive performance. The results demonstrated that the 5 T cell-related genes model could significantly distinguish the survival outcomes of patients in the GSE39582 cohort and exhibited good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-G). The validation results in the GSE41258 cohort also supported the same conclusion, confirming the robustness of the 5 T cell-related genes model in predicting CRC patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, scRNA-seq is widely used to explore the molecular features of tumor-infiltrating immune cells in the TME and T cells have received significant attention in the prognosis of cancers [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In CRC, the abundance of tumor-infiltrating T cells is closely related to the prognosis of patients [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], indicating that it is a laudable attempt to construct a prognostic model based on T cell marker genes. Recently, Guo et al. comprehensively analyzed subpopulations, cell-cell interaction and trajectory of T cells in the TME and built a prognostic risk model using T cell marker genes to evaluate individual survival of patients with triple-negative breast cancer [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Inspired by this research, we aimed to identify the T cell marker genes with prognostic potential, and developed novel molecular classifiers based on these genes to guide future clinical management of CRC patients.\u003c/p\u003e \u003cp\u003eIn this study, we systematically investigated the heterogeneity and functional changes of various cells in the tumor microenvironment of CRC patients through the reintegration and reanalysis of single-cell datasets. In particular, we investigated the alterations in cell-cell communications within normal colon and CRC tissues, and revealed the functional shift in tumor-infiltrating T cells induced by tumor cells via the MIF/CD74 signaling pathway. While additional confirmation is required, the inhibitory effects of the MIF/CD74 signaling pathway on T cells have been extensively documented in various types of tumors [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Our findings are consistent with existing reports and highlight the oncogenic role of the MIF/CD74 signaling pathway in CRC. Furthermore, through pseudo-time analysis, we investigated the evolutionary trajectory of T cells and revealed the molecular alterations underlying the transition from naive T cells to exhausted T cells in CRC tissues. These findings provide a novel theoretical basis for immunotherapy and targeted therapy in CRC. Eventually, we integrated single-cell and bulk RNA sequencing analysis to construct a prognostic prediction model based on T cell-related genes via multivariable Cox regression algorithm. The validation results from several independent CRC cohorts indicated that the 5 T cell-related genes prognostic model could accurately predict the survival outcomes of CRC patients, providing new evidence for precision treatment in CRC.\u003c/p\u003e \u003cp\u003eAmong the five genes, GADD45B, SLC2A3 and TERF2IP were previously reported to be tightly associated with CRC. GADD45B is a member of the growth arrest and DNA damage-inducible gene family, involved in cell growth control, DNA damage response and apoptosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. GADD45B is upregulated in CRC tissues, and its high expression is indicative of short overall survival and disease-free survival [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. SLC2A3 is a member of the solute carrier family whose function is to transport glucose [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. SLC2A3 is highly expressed in CRC and negatively correlated with the prognosis of CRC patients. SLC2A3 promotes invasiveness and cancer stem cell-like properties of CRC cells through forming a positive regulatory loop with the Hippo cascade transducer Yes-associated protein [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Moreover, SLC2A3 fosters the growth of CRC cells by expediting glucose input, upregulating aerobic glycolysis and facilitating nucleotide synthesis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. TERF2IP is a crucial component of shelterin and it bolsters CRC cell invasion by activating the MAPK signaling pathway [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Although SLC20A1 has not been reported in CRC, its prognostic value in breast cancer, esophageal adenocarcinoma and pancreatic cancer has been demonstrated [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The MRPL22 gene has not been reported in cancer.\u003c/p\u003e \u003cp\u003eDespite the promising findings obtained, our research has some limitations. Firstly, the study was retrospective, with transcriptome data downloaded from publicly available GEO databases, thus the current findings need to be validated in prospective and real-word studies. Secondly, predictive performance of the 5-gene signature at the protein level deserves further investigation. In addition, the biological functions and potential mechanisms of SLC20A1 and MRPL22 in CRC remain unclear. Future efforts should focus on verifying the efficacy of the model through in-house cohorts, exploring the molecular mechanisms of SLC20A1 and MRPL22, and providing experimental basis for the clinical application of our prognostic model.\u003c/p\u003e \u003cp\u003eIn conclusion, we analyzed scRNA-seq data of CRC tissues and proposed a novel prognostic signature based on T-cell marker genes. This signature exhibited moderate predictive performance and might help to guide clinical decision. Our study provides a novel insight into the role of immune cell marker genes in cancer prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCNV = Copy number variation;\u003c/p\u003e\n\u003cp\u003eCRC = colorectal cancer;\u003c/p\u003e\n\u003cp\u003eDEG = differentially expressed genes;\u003c/p\u003e\n\u003cp\u003eGEO = Gene Expression Omnibus;\u003c/p\u003e\n\u003cp\u003eGO = gene ontology;\u003c/p\u003e\n\u003cp\u003eGSEA = Gene set enrichment analysis;\u003c/p\u003e\n\u003cp\u003eKEGG = Kyoto Encyclopedia of Genes and Genomes;\u003c/p\u003e\n\u003cp\u003eK-M = Kaplan\u0026ndash;Meier;\u003c/p\u003e\n\u003cp\u003eKNN = K-nearest neighbors;\u003c/p\u003e\n\u003cp\u003eNES = normalized enrichment score;\u003c/p\u003e\n\u003cp\u003ePCA = principal component analysis;\u003c/p\u003e\n\u003cp\u003eROC = receiver operating characteristic;\u003c/p\u003e\n\u003cp\u003eScRNA-seq = Single-cell RNA-sequencing;\u003c/p\u003e\n\u003cp\u003eTCGA = The Cancer Genome Atlas;\u003c/p\u003e\n\u003cp\u003eTME = tumor microenvironment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted with the approval of the Ethics Committee of Shanghai Changhai Hospital\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e National Natural Science Foundation of China(82203137, 82072750, 82272705), Shanghai Sailing Program(21YF1459300), 71st Batch of China Postdoctoral Science Foundation (48804), Three-year action plan to promote clinical skills and clinical innovation in municipal hospitals(SHDC2022CRT007)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eWei Zhang, Guanyu Yu and Fuao Cao designed the research. Xiaoming Zhu, Rongbo Wen, Jiaqi Wu and Leqi Zhou made substantial contributions to acquisition, analysis and interpretation of data, and wrote the manuscript. Hao Fan, Tianshuai Zhang, Yiyang Li and Zixuan Liu coordinated and were involved in acquisition, interpretation of the data. Wei Zhang, Guanyu Yu and Fuao Cao revised it critically for important intellectual content and gave final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026rsquo;s findings are available on request from the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets presented in this study are included in the article/supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank The National Natural Science Foundation of China for the grant funding. We acknowledge the contributions from the GEO and TCGA databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Wagle NS, Cercek A, Smith RA, Jemal A: Colorectal cancer statistics, 2023. CA: a cancer journal for clinicians 2023, 73(3):233\u0026ndash;254.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuipers EJ, Grady WM, Lieberman D, Seufferlein T, Sung JJ, Boelens PG, van de Velde CJ, Watanabe T: Colorectal cancer. Nature reviews Disease primers 2015, 1:15065.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB: Colorectal cancer. Lancet (London, England) 2019, 394(10207):1467\u0026ndash;1480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrody H: Colorectal cancer. 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Genome medicine 2014, 6(12):105.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Single-cell RNA sequencing, Bulk RNA-sequencing, T cell marker gene, Prognostic signature","lastPublishedDoi":"10.21203/rs.3.rs-3909225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eT cells, the key mediators of tumor destruction, have a considerable impact on tumor prognosis. However, the clinical significance of T cell-associated biomarkers in colorectal cancer (CRC) haven’t been well understood. The aim of this study was to investigate the expression profile of T cell marker genes in CRC and develop a prognostic signature based on these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eSingle-cell RNA-sequencing (scRNA-seq) data were retrieved from the Gene Expression Omnibus (GEO) database. Bulk RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases. We firstly conducted a comprehensive analysis of scRNA-seq data to investigate the heterogeneity of various cells in the CRC tumor microenvironment (TME). Then, we performed cell-cell communication analysis and cell trajectory analysis to explore the intercellular interactions and functional changes of T cells. By combing the bulk RNA-seq data, a T-cell related gene signature was eventually constructed and its predictive ability was determined by the Kaplan–Meier (K-M), and receiver operating characteristic (ROC) curves in three independent cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e ScRNA-seq data obtained from the GEO database were re-integrated and analyzed, resulting in 23 cell clusters. Distinct cell clusters were annotated using extensively reported cell markers. The CellChat algorithm revealed that tumor cells suppress the cellular function of tumor-infiltrating T cells through the MIF/CD74 pathway. The evolutionary trajectory of tumor-infiltrating T cells was elucidated by the CytoTRACE and monocle2 algorithms. Eventually, a prognostic prediction model based on 5 T cell-related genes was constructed using single-cell and bulk RNA sequencing data. The validation results from several independent CRC cohorts indicated that the 5 T cell-related genes prognostic model could accurately predict the survival outcomes of CRC patients, providing new evidence for precision treatment in CRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study not only offers prospects for a better understanding of the cellular heterogeneity of TME, but also provides a useful tool for stratifying patients with different prognoses and facilitating personalized treatment.\u003c/p\u003e","manuscriptTitle":"Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 19:11:04","doi":"10.21203/rs.3.rs-3909225/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fce9170-5f73-4862-a8d3-f17688b176c3","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28468275,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":28468276,"name":"Biological sciences/Cancer/Gastrointestinal cancer"}],"tags":[],"updatedAt":"2024-02-13T10:56:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 19:11:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909225","identity":"rs-3909225","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}