CXCR6 positions cytotoxic T cells to receive critical survival signals in the tumor microenvironment.

Cytotoxic T lymphocyte (CTL) responses against tumors are maintained by stem-like memory cells that self-renew but also give rise to effector-like cells. The latter gradually lose their anti-tumor activity and acquire an epigenetically fixed, hypofunctional state, leading to tumor tolerance. Here, we show that the conversion of stem-like into effector-like CTLs involves a major chemotactic reprogramming that includes the upregulation of chemokine receptor CXCR6. This receptor positions effector-like CTLs in a discrete perivascular niche of the tumor stroma that is densely occupied by CCR7+ dendritic cells (DCs) expressing the CXCR6 ligand CXCL16. CCR7+ DCs also express and trans-present the survival cytokine interleukin-15 (IL-15). CXCR6 expression and IL-15 trans-presentation are critical for the survival and local expansion of effector-like CTLs in the tumor microenvironment to maximize their anti-tumor activity before progressing to irreversible dysfunction. These observations reveal a cellular and molecular checkpoint that determines the magnitude and outcome of anti-tumor immune responses.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters.

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Oncogenic Signaling Pathways in The Cancer Genome Atlas.

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFß signaling, p53 and ß-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma.

Diffuse large B cell lymphoma (DLBCL) is the most common form of blood cancer and is characterized by a striking degree of genetic and clinical heterogeneity. This heterogeneity poses a major barrier to understanding the genetic basis of the disease and its response to therapy. Here, we performed an integrative analysis of whole-exome sequencing and transcriptome sequencing in a cohort of 1,001 DLBCL patients to comprehensively define the landscape of 150 genetic drivers of the disease. We characterized the functional impact of these genes using an unbiased CRISPR screen of DLBCL cell lines to define oncogenes that promote cell growth. A prognostic model comprising these genetic alterations outperformed current established methods: cell of origin, the International Prognostic Index comprising clinical variables, and dual MYC and BCL2 expression. These results comprehensively define the genetic drivers and their functional roles in DLBCL to identify new therapeutic opportunities in the disease.

Germline genetic contribution to the immune landscape of cancer.

Understanding the contribution of the host's genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-ß-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.

A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes.

Exitron splicing (EIS) creates a cryptic intron (called an exitron) within a protein-coding exon to increase proteome diversity. EIS is poorly characterized, but emerging evidence suggests a role for EIS in cancer. Through a systematic investigation of EIS across 33 cancers from 9,599 tumor transcriptomes, we discovered that EIS affected 63% of human coding genes and that 95% of those events were tumor specific. Notably, we observed a mutually exclusive pattern between EIS and somatic mutations in their affected genes. Functionally, we discovered that EIS altered known and novel cancer driver genes for causing gain- or loss-of-function, which promotes tumor progression. Importantly, we identified EIS-derived neoepitopes that bind to major histocompatibility complex (MHC) class I or II. Analysis of clinical data from a clear cell renal cell carcinoma cohort revealed an association between EIS-derived neoantigen load and checkpoint inhibitor response. Our findings establish the importance of considering EIS alterations when nominating cancer driver events and neoantigens.

Study of prognostic splicing factors in cancer using machine learning approaches.

Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing the generation of oncogenic proteins involved in cancer hallmarks. In this study, we investigated the genes that encode RNA-binding proteins and identified potential splicing factors that contribute to the aberrant splicing applying a random forest classification model. The result suggested 56 splicing factors were related to the prognosis of 13 cancers, two SF complexes in liver hepatocellular carcinoma, and one SF complex in esophageal carcinoma. Further systematic bioinformatics studies on these cancer prognostic splicing factors and their related alternative splicing events revealed the potential regulations in a cancer-specific manner. Our analysis found high ILF2-ILF3 expression correlates with poor prognosis in LIHC through alternative splicing. These findings emphasize the importance of SFs as potential indicators for prognosis or targets for therapeutic interventions. Their roles in cancer exhibit complexity and are contingent upon the specific context in which they operate. This recognition further underscores the need for a comprehensive understanding and exploration of the role of SFs in different types of cancer, paving the way for their potential utilization in prognostic assessments and the development of targeted therapies.

Elevated incidence of somatic mutations at prevalent genetic sites.

The common loci represent a distinct set of the human genome sites that harbor genetic variants found in at least 1% of the population. Small somatic mutations occur at the common loci and non-common loci, i.e. csmVariants and ncsmVariants, are presumed with similar probabilities. However, our work revealed that within the coding region, common loci constituted only 1.03% of all loci, yet they accounted for 5.14% of TCGA somatic mutations. Furthermore, the small somatic mutation incidence rate at these common loci was 2.7 times that observed in the non-common. Notably, the csmVariants exhibited an impressive recurrent rate of 36.14%, which was 2.59 times of the ncsmVariants. The C-to-T transition at the CpG sites accounted for 32.41% of the csmVariants, which was 2.93 times for the ncsmVariants. Interestingly, the aging-related mutational signature contributed to 13.87% of the csmVariants, 5.5 times that of ncsmVariants. Moreover, 35.93% of the csmVariants contexts exhibited palindromic features, outperforming ncsmVariant contexts by 1.84 times. Notably, cancer patients with higher csmVariants rates had better progression-free survival. Furthermore, cancer patients with high-frequency csmVariants enriched with mismatch repair deficiency were also associated with better progression-free survival. The accumulation of csmVariants during cancerogenesis is a complex process influenced by various factors. These include the presence of a substantial percentage of palindromic sequences at csmVariants sites, the impact of aging and DNA mismatch repair deficiency. Together, these factors contribute to the higher somatic mutation incidence rates of common loci and the overall accumulation of csmVariants in cancer development.

ACLY alternative splicing correlates with cancer phenotypes.

ATP-citrate lyase (ACLY) links carbohydrate and lipid metabolism and provides nucleocytosolic acetyl-CoA for protein acetylation. ACLY has two major splice isoforms: the full-length canonical "long" isoform and an uncharacterized "short" isoform in which exon 14 is spliced out. Exon 14 encodes 10 amino acids within an intrinsically disordered region and includes at least one dynamically phosphorylated residue. Both isoforms are expressed in healthy tissues to varying degrees. Analysis of human transcriptomic data revealed that the percent spliced in (PSI) of exon 14 is increased in several cancers and correlated with poorer overall survival in a pan-cancer analysis, though not in individual tumor types. This prompted us to explore potential biochemical and functional differences between ACLY isoforms. Here, we show that there are no discernible differences in enzymatic activity or stability between isoforms or phosphomutants of ACLY in vitro. Similarly, both isoforms and phosphomutants were able to rescue ACLY functions, including fatty acid synthesis and bulk histone acetylation, when re-expressed in Acly knockout cells. Deletion of Acly exon 14 in mice did not overtly impact development or metabolic physiology nor did it attenuate tumor burden in a genetic model of intestinal cancer. Notably, expression of epithelial splicing regulatory protein 1 (ESRP1) is highly correlated with ACLY PSI. We report that ACLY splicing is regulated by ESRP1. In turn, both ESRP1 expression and ACLY PSI are correlated with specific immune signatures in tumors. Despite these intriguing patterns of ACLY splicing in healthy and cancer tissues, functional differences between the isoforms remain elusive.

A heavy-tailed model for analyzing miRNA-seq raw read counts.

This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions is proposed as a novel approach to better capture the heterogeneity and extreme values commonly observed in miRNA-seq data. Additionally, the parameters of the discrete stable distribution are proposed as an alternative target for differential expression analysis. An R package for computing and estimating the discrete stable distribution is provided. The proposed model is applied to miRNA-seq raw counts from the Norwegian Women and Cancer Study (NOWAC) and the Cancer Genome Atlas (TCGA) databases. The goodness-of-fit is compared with the popular Poisson and negative binomial distributions, and the discrete stable distributions are found to give a better fit for both datasets. In conclusion, the use of discrete stable distributions is shown to potentially lead to more accurate modeling of the underlying biological processes.

Role of autophagy-related genes in liver cancer prognosis.

Autophagy, a highly conserved process of protein and organelle degradation, has emerged as a critical regulator in various diseases, including cancer progression. In the context of liver cancer, the predictive value of autophagy-related genes remains ambiguous. Leveraging chip datasets from the TCGA and GTEx databases, we identified 23 differentially expressed autophagy-related genes in liver cancer. Notably, five key autophagy genes, PRKAA2, BIRC5, MAPT, IGF1, and SPNS1, were highlighted as potential prognostic markers, with MAPT showing significant overexpression in clinical samples. In vitro cellular assays further demonstrated that MAPT promotes liver cancer cell proliferation, migration, and invasion by inhibiting autophagy and suppressing apoptosis. Subsequent in vivo studies further corroborated the pro-tumorigenic role of MAPT by suppressing autophagy. Collectively, our model based on the five key genes provides a promising tool for predicting liver cancer prognosis, with MAPT emerging as a pivotal factor in tumor progression through autophagy modulation.

STRN3 promotes tumour growth in hepatocellular carcinoma by inhibiting the hippo pathway.

HCC is a globally high-incidence malignant tumour, and its pathogenesis is still unclear. Recently, STRN3 has been found to be elevated in various tumours, but its expression and biological functions in HCC have not been studied. In the study, clinical correlation analysis was performed on 371 liver cancer patients from TCGA database and liver cancer tissues and normal tissues from the GEO database. qRT-PCR and western blotting were used to detect relevant proteins in cells, and CCK8 and colony formation experiments were performed to analyse cell proliferation ability. Transwell and wound healing experiments were performed to detect cell invasion ability, and flow cytometry was used to detect cell apoptosis. Single-cell sequencing data and multiple immunofluorescence were analysed for the expression abundance and distribution of certain proteins. Immunohistochemistry was used to assess the expression of STRN3 in patients' tumour and adjacent non-cancerous tissues. The results indicated STRN3 was highly expressed in liver tumour tissues and was closely associated with poor prognosis. Knockdown of STRN3 could significantly inhibit cell proliferation and migration ability. At the same time, we found that STRN3 could inhibit the Hippo pathway and promote the entry of YAP protein into the nucleus. Our study first found that STRN3 could promote tumour growth by inhibiting the Hippo pathway. The study of STRN3 can promote the understanding and treatment of the occurrence and development of HCC.

A novel mitochondria-related core gene signature to predict the prognosis and evaluate tumour microenvironment in CESC single-cell validation.

Mitochondria and their related genes (MTRGs) are pivotal in the tumour microenvironment (TME) of cervical cancer, influencing prognosis and treatment response. This study developed a prognostic model using MTRGs to predict overall survival (OS) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), aiming for personalized therapy. Analysing 14 MTRGs like ISCU and NDUFA11 through techniques such as univariate Cox regression, we found that a low mitochondrial (MT) score is associated with better survival, while a high MT score predicts poorer outcomes. The TME score, particularly influenced by CD8 T cells, also correlates with prognosis, with a high score indicating favourable outcomes. The interplay between MT and TME subtypes revealed that the best prognosis is seen in patients with a low MT and high TME score. Our findings highlight the role of MTRGs as potential biomarkers and therapeutic targets in cervical cancer, offering a novel approach to improving patient outcomes through a more nuanced understanding of mitochondrial function and immune interactions within the TME. This model presents a promising avenue for enhancing the precision of prognostic assessments in CESC.

Transcriptomic analysis of Porphyromonas gingivalis-infected head and neck cancer cells: Identification of PLAU as a candidate prognostic biomarker.

Periodontal disease is a risk factor for head and neck squamous cell carcinoma (HNSCC), and Porphyromonas gingivalis, a major periodontal pathogen, has been identified as a specific and potentially independent microbial factor that increases the risk of cancer mortality. Gene expression in HNSCC due to P. gingivalis infection and how changes in gene expression affect the prognosis of HNSCC patients are not clarified. When P. gingivalis was cultured with HNSCC cells, it efficiently adhered to these cells and enhanced their invasive ability. A transcriptome analysis of P. gingivalis -infected HNSCC cells showed that genes related to migration, including CCL20, CITED2, CTGF, C8orf44-SGK3, DUSP10, EGR3, FUZ, HBEGF, IL1B, IL24, JUN, PLAU, PTGS2, P2RY1, SEMA7A, SGK1 and SIX2, were highly up- or down-regulated. The expression of up-regulated genes was examined using the expression data of HNSCC patients obtained from The Cancer Genome Atlas (TCGA) database, and the expression of 5 genes, including PLAU, was found to be higher in cancer tissue than in solid normal tissue. An analysis of protein-protein interactions revealed that these 5 genes formed a dense network. A Cox regression analysis showed that high PLAU expression levels were associated with a poor prognosis in patients with TCGA-HNSCC. Furthermore, the prognostic impact correlated with tumour size and the presence or absence of lymph node metastasis. Collectively, these results suggest the potential of PLAU as a molecular prognostic marker in HNSCC patients. Further in vivo and in vitro studies are needed to verify the findings of this study.

Association between Homologous Recombination Repair Defect Status and Long-Term Prognosis of Early HER2-Low Breast Cancer: A Retrospective Cohort Study.

BACKGROUND: As a newly identified subtype of HER2-negative tumors associated with a less favorable prognosis, it remains crucial to evaluate potential prognostic and predictive factors, particularly non-invasive biomarkers, for individuals with human epidermal growth factor 2 (HER2) low early-stage breast cancer (EBC). Multiple investigations have highlighted that HER2-negative patients with EBC exhibiting high homologous recombination deficiency (HRD) scores display lower rates of pathological complete response (PCR) to neoadjuvant chemotherapy (NAC). Nevertheless, no study to date has explored the correlation between HRD and the long-term prognosis in HER2-low patients with EBC. PATIENTS AND METHODS: This retrospective observational study focuses on primary EBC sourced from The Cancer Genome Atlas dataset (TCGA). It reveals the gene mutation landscape in EBC with low HER2 expression and elucidates the tumor immune landscape across different HRD states. Utilizing bioinformatics analysis and Cox proportional models, along with the Kaplan-Meier method, the study assesses the correlation between HRD status and disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Subgroup analyses were conducted to identify potential variations in the association between HRD and prognosis. RESULTS: In the patients with HER2-low breast cancer, patients with homologous recombination related genes (HRRGs) defects had an HRD score about twice that of those without related genes mutations, and were at higher risk of acquiring ARID1A, ATM, and BRCA2 mutations. We also found that most immune cell abundances were significantly higher in EBC tumors with high HRD than in EBC tumors with low HRD or HRD-medium, particularly plasma B-cell abundance, CD8 T-cell abundance, and M1 macrophages. In addition, these tumors with HRD-high also appear to have significantly higher tumor immune scores and lower interstitial scores. Then, we analyzed the relationship between different HRD status and prognosis. There was statistical significance (P = .036 and P = .046, respectively) in DSS and PFI between the HRD-low and HRD-high groups, and patients with HRD-high EBC showed relatively poor survival outcomes. A medium HRD score (hazard ratio, HR = 2.15, 95% CI: 1.04-4.41, P = .038) was a significant risk factor for PFI. Hormone receptor positivity is an important factor in obtaining medium-high HRD score and poor prognosis. CONCLUSION: Higher HRD scores were associated with poorer PFI outcomes, particularly in people with HR+/HER2-low. Varied HRD states exhibited distinctions in HRRGs and the tumor immune landscape. These insights have the potential to assist clinicians in promptly identifying high-risk groups and tailoring personalized treatments for patients with HER2-low EBC, aiming to enhance long-term outcomes.

NLRP12 is a prognostic biomarker and correlated with immune infiltrates in epithelial ovarian cancer.

BACKGROUND: NLRP12 is a member of the intracellular Nod-like receptor (NLR) family, suggesting it is an innate immune receptor for the initiation and progression of several cancers. However, its role on prognosis and immune infiltrates in epithelial ovarian cancer (EOC) is still unknown. The present study aimed to evaluate its prognostic value and its association with immune infiltrates in EOC. METHODS: The mRNA expression of NLRP12 of EOC from The Cancer Genome Atlas (TCGA) was analyzed. The association between NLRP12 and clinicopathological characters was evaluated with logistic regression. The association between NLRP12 expression and survival was analyzed by Cox regression and Kaplan-Meier analyses. A nomogram was used to predict the impact of NLRP12 on prognosis. Gene Ontology term analysis and gene set enrichment analysis (GSEA) were performed to identify the signaling pathways related to NLRP12 expression. Immune cells infiltration for NLRP12 was analyzed using single-sample GSEA. The relationship between NLRP12 and tumor-infiltrating immune cells (TICs) was investigated by a Wilcoxon rank sum test. The expression of NLRP12 were also further verified in EOC tissues and cell lines. Additionally, we confirmed the biological function of NLRP12 in vitro. RESULTS: NLRP12 was highly expressed in patients with EOC from TCGA. High NLRP12 expression correlated with poor disease-specific survival (p < 0.001) and overall survival (p < 0.001). Multivariate analysis revealed that NLRP12 expression was an independent prognostic marker for overall survival (p = 0.042). The C-indexes and calibration plots of the nomogram based on multivariate analysis indicated an effective predictive performance for EOC patients. GSEA showed enrichment of cell adhesion, tumorigenesis and immune response in the NLRP12 high expression group. Increased NLRP12 expression correlated positively with several TICs, including macrophages, neutrophils, T effector memory cells and immature dendritic cells (p < 0.001). In addition, NLRP12 silencing inhibited cell proliferation and migration in EOC cells. CONCLUSIONS: In conclusion, increased NLRP12 expression correlated significantly with poor survival and immune infiltration in EOC.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC. METHODS: We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. RESULTS: Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. CONCLUSION: We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.