Advances in Immunotherapy and the TGF-β Resistance Pathway in Metastatic Bladder Cancer

The prognostic significance and immune characteristics of bone morphogenetic proteins (BMPs) family: A pan-cancer multi-omics analysis

BACKGROUND

Bone morphogenetic proteins (BMPs) are a group of cancer-related proteins vital for development and progression of certain cancer types. Nevertheless, function of BMP family in pan-cancer was not detailedly researched.

OBJECTIVE

Investigating expression pattern and prognostic value of the BMPs family (BMP1-8A and BMP8B) expression across multiple cancer types.

METHODS

Our research integrated multi-omics data for exploring potential associations between BMPs expression and prognosis, clinicopathological characteristics, copy number or somatic mutations, immune characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint genes and drug sensitivity in The Cancer Genome Atlas (TCGA) tumors. Furthermore, association of BMPs expression and immunotherapy effectiveness was investigated in some confirmatory cohorts (GSE111636, GSE78220, GSE67501, GSE176307, IMvigor210 and mRNA sequencing data from currently undergoing TRUCE01 clinical research included), and biological function and potential signaling pathways of BMPs in bladder cancer (BCa) was explored via Gene Set Enrichment Analysis (GSEA). Eventually, immune infiltration analysis was done via BMPs expression, copy number or somatic mutations in BCa, as well as validation of the expression levels by reverse transcription-quantitative PCR and western blot, and in vitro functional experiments of BMP8A.

RESULTS

Discoveries displayed BMPs expression was related to prognosis, clinicopathological characteristics, mutations, TME, TMB, MSI and immune checkpoint genes of TCGA tumors. Anticancer drug sensitivity analysis displayed BMPs were associated with various drug sensitivities. What's more, it was discovered that expression level of certain BMP family members related to objective response to immunotherapy. By GSEA, we discovered multiple immune-associated functions and pathways were enriched. Immune infiltration analysis on BCa also displayed significant associations among BMPs copy number variations, mutation status and infiltration level of diverse immune cells. Furthermore, differential expression validation and in vitro phenotypic experiment indicated that BMP8A significantly promoted BCa cell proliferation, migration and invasion.

CONCLUSIONS

Current results confirmed significance of both BMPs expression and genomic alteration in the prognosis and treatment of diverse cancer types, and suggested that BMPs may be vital for BCa and can possibly be utilized as biomarkers for immunotherapy.

Exploring the microRNA-mRNA regulatory network associated with solasonine in bladder cancer

Background

Solasonine has been demonstrated to exert an inhibitory effect on bladder cancer (BC), but the potential mechanisms remain unclear. Therefore, the aim of this study is to explore the association between microRNAs (miRNAs)-mediated regulation and the anti-tumor activities of solasonine in BC.

Methods

MiRNA sequencing was performed to identify the differentially expressed microRNAs (DE-miRNAs) associated with solasonine in BC cells. Functional enrichment analyses of the DE-miRNAs activated and inhibited by solasonine were then conducted. The DE-miRNAs with prognostic value for BC and those differentially expressed in the BC samples were subsequently identified as the hub DE-miRNAs. After identifying the messenger RNAs (mRNAs) that were targeted by the hub DE-miRNAs and those differentially expressed in the BC samples, a protein-protein interaction analysis was performed to identify the core downstream genes, which were then used to construct a solasonine-miRNA-mRNA regulatory network.

Results

A total of 27 activated and 19 inhibited solasonine-mediated DE-miRNAs were identified that were found to be associated with several tumor-related biological functions and pathways. After integrating the results of the survival analysis and expression assessment, the following nine hub DE-miRNAs were identified: hsa-miR-127-3p, hsa-miR-450b-5p, hsa-miR-99a-5p, hsa-miR-197-3p, hsa-miR-423-3p, hsa-miR-4326, hsa-miR-625-3p, hsa-miR-625-5p, and hsa-miR-92a-3p. The DE-mRNAs targeted by the hub DE-miRNAs were predicted, and 30 core downstream genes were used to construct the solasonine-miRNA-mRNA regulatory network. miR-450b-5p was shown to be associated with the most mRNAs in this network, which suggests that it plays a crucial role in the solasonine-mediated anti-BC effect.

Conclusions

A regulatory network, including solasonine, miRNAs, and mRNAs related to BC, was constructed. This network provides extensive insights into the molecular regulatory mechanisms that underlie the anti-cancer efficacy of solasonine in BC.

Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer

BACKGROUND

Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research aims to predict prognosis and identify precise therapeutic targets for novel treatment interventions.

METHODS

We collected and screened genes related to the TGF-β signaling pathway and performed unsupervised clustering analysis on TCGA-BLCA samples based on these genes. Our analysis revealed two novel subtypes of bladder cancer with completely different biological characteristics, including immune microenvironment, drug sensitivity, and more. Using machine learning classifiers, we identified SMAD6 as a hub gene contributing to these differences and further investigated the role of SMAD6 in bladder cancer in the single-cell transcriptome data. Additionally, we analyzed the relationship between SMAD6 and immune checkpoint genes. Finally, we performed a series of in vitro assays to verify the function of SMAD6 in bladder cancer cell lines.

RESULTS

We have revealed two novel subtypes of bladder cancer, among which C1 exhibits a worse prognosis, lower drug sensitivity, a more complex tumor microenvironment, and a 'colder' immune microenvironment compared to C2. We identified SMAD6 as a key gene responsible for the differences and further explored its impact on the molecular characteristics of bladder cancer. Through in vitro experiments, we found that SMAD6 promoted the prognosis of BCa patients by inhibiting the proliferation and migration of BCa cells.

CONCLUSION

Our study reveals two novel subtypes of BCa and identifies SMAD6 as a highly promising therapeutic target.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Moscatilin Reverses EMT Progression and its Resulting Enhanced Invasion and Migration by Affecting the TGF-β Signaling Pathway in Bladder Cancer

BACKGROUND

Bladder cancer metastasis is an essential process in the progression of muscle-invasive bladder cancer. EMT plays a crucial role in facilitating the spread of cancer cells. Identifying compounds that can inhibit these abilities of cancer cells is a significant international endeavor.

OBJECTIVE

To explore the migration and invasion effect of Moscatilin on the bladder and clarify the mechanism of action Methods: The anti-bladder cancer effect of Moscatilin was observed by a cell proliferation experiment. The migration and invasion of bladder cancer cells inhibited by Moscatilin were detected by Transwell and Wound healing. The effects of Moscatilin on EMT-related proteins E-cadherin, N-cadherin, Snail1, Vimentin, and TGF-β signaling pathways were detected by Western blot, and nucleic acid levels were verified by qPCR.

RESULTS

Our study revealed that Moscatilin reduced the viability of bladder cancer cells in vitro and impeded their migration and invasion in experimental settings. Furthermore, we observed that Moscatilin decreased the activation levels of active proteins, specifically Smad3, Samd2, and MMP2. Additionally, we found that moscatilin significantly reduced the expression level of TGF-β and was also capable of reversing the overexpression effect of TGF-β. Treatment with Moscatilin also led to significant inhibition of interstitial cell markers Ncadherin and Snail1, which are associated with EMT.

CONCLUSION

These findings indicate that Moscatilin impedes the migration and invasion of bladder cancer cells by influencing cell survival, modulating TGF-β/Smad signaling, and inhibiting EMT.

The bladder cancer immune micro-environment in the context of response to immune checkpoint inhibition

Treatment with neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy is the default treatment for muscle-invasive bladder cancer (BC). However, with the encouraging results of immune checkpoint inhibitiors (ICI) directed against PD-1/PD-L1 and CTLA-4 in recent years, the treatment landscape of BC is rapidly changing. In addition, it is becoming clear that the effect of ICI is highly dependent on the interaction between tumor cells and the tumor immune micro-environment (TIME). Different immune cells are involved in an anti-tumor response in BC. Cytotoxic CD8+ T-cells are the main effector cells, aided by other immune cells including other T-cells, B-cells and pro-inflammatory macrophages. As part of the ongoing anti-tumor immune response, lymphocytes aggregate in clusters called tertiary lymphoid structures (TLS). Tumor mutational burden (TMB) and infiltration of immune cells into the tumor are both important factors for establishing an anti-tumor immune response. In contrast, transforming growth factor beta (TGF-β) signaling in cancer-associated fibroblasts (CAFs) prevents infiltration of lymphocytes and potentially has an immunosuppressive effect. In conclusion, the effect of ICI seems to be reliant on a combination of tumor-intrinsic and TIME-related parameters. More research is needed to fully understand the underlying biological mechanisms to further improve patient care.

Immunotherapy in urothelial cancer: current status and future directions

INTRODUCTION

Since 2016, the progressive use of immune checkpoint inhibitors (ICIs) starting from second-line treatment has led to an improvement in overall survival in locally advanced and metastatic urothelial cancer (UC). Clinical trials are underway testing the role of ICIs since the first stages of the disease, alone or in combination with standard therapies.

AREAS COVERED

This review summarizes the current updated evidence regarding the role of ICIs in the different stages of UC, the ongoing clinical trials exploring the potential benefit of immunotherapy alone or in combination with standard-of-care therapies, as well as the promising association of ICIs with antibody-drug conjugates (ADCs).

EXPERT OPINION

In the first-line setting, ICIs alone in platinum-unfit patients have shown unconvincing results; the ongoing EV-302 trial will probably suggest enfortumab vedotin plus pembrolizumab as a new effective option. The optimal duration of maintenance immunotherapy is still to be determined, finding a balance with the risk-benefit profile. The clinical benefit of ICIs as second-line treatment is limited to a subset of patients that cannot be definitively established yet. In the next 5 years, a lot of new ADCs will likely emerge for the treatment of UC.

Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinical analysis validation

Introduction

Bladder cancer (BLCA) is one of the most lethal diseases. COL10A1 is secreted small-chain collagen in the extracellular matrix associated with various tumors, including gastric, colon, breast, and lung cancer. However, the role of COL10A1 in BLCA remains unclear. This is the first research focusing on the prognostic value of COL10A1 in BLCA. In this research, we aimed to uncover the association between COL10A1 and the prognosis, as well as other clinicopathological parameters in BLCA.

Methods

We obtained gene expression profiles of BLCA and normal tissues from the TCGA, GEO, and ArrayExpress databases. Immunohistochemistry staining was performed to investigate the protein expression and prognostic value of COL10A1 in BLCA patients. GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. We used the “maftools” R package to display the mutation profiles between the high and low COL10A1 groups. GIPIA2, TIMER, and CIBERSORT algorithms were utilized to explore the effect of COL10A1 on the tumor immune microenvironment.

Results

We found that COL10A1 was upregulated in the BLCA samples, and increased COL10A1 expression was related to poor overall survival. Functional annotation of 200 co-expressed genes positively correlated with COL10A1 expression, including GO, KEGG, and GSEA enrichment analyses, indicated that COL10A1 was basically involved in the extracellular matrix, protein modification, molecular binding, ECM-receptor interaction, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway. The most commonly mutated genes of BLCA were different between high and low COL10A1 groups. Tumor immune infiltrating analyses showed that COL10A1 might have an essential role in recruiting infiltrating immune cells and regulating immunity in BLCA, thus affecting prognosis. Finally, external datasets and biospecimens were used, and the results further validated the aberrant expression of COL10A1 in BLCA samples.

Conclusions

In conclusion, our study demonstrates that COL10A1 is an underlying prognostic and predictive biomarker in BLCA.

Cancer-associated Fibroblasts in Bladder Cancer: Origin, Biology, and Therapeutic Opportunities

CONTEXT

Bladder cancer (BLCA) is a highly prevalent tumour and a health problem worldwide, especially among men. Recent work has highlighted the relevance of the tumour microenvironment (TME) in cancer biology with translational implications. Cancer-associated fibroblasts (CAFs) are a prominent, heterogeneous population of cells in the TME. CAFs have been associated with tumour development, progression, and poor prognosis in several neoplasms. However, their role in BLCA has not yet been exploited deeply.

OBJECTIVE

To review the role of CAFs in BLCA biology and provide an understanding of CAF origin, subtypes, markers, and phenotypic and functional characteristics to improve patient management.

EVIDENCE ACQUISITION

A PubMed search was performed to review manuscripts published using the terms "cancer associated fibroblast" and "bladder cancer" or "urothelial cancer". All abstracts were reviewed, and the full content of all relevant manuscripts was analysed. In addition, selected manuscripts on CAFs in other tumours were considered.

EVIDENCE SYNTHESIS

CAFs have been studied less extensively in BLCA than in other tumours. Thanks to new techniques, such as single-cell RNA-seq and spatial transcriptomics, it is now possible to accurately map and molecularly define the phenotype of fibroblasts in normal bladder and BLCA. Bulk transcriptomic analyses have revealed the existence of subtypes among both non-muscle-invasive and muscle-invasive BLCA; these subtypes display distinct features regarding their CAF content. We provide a higher-resolution map of the phenotypic diversity of CAFs in these tumour subtypes. Preclinical studies and recent promising clinical trials leverage on this knowledge through the combined targeting of CAFs or their effectors and the immune microenvironment.

CONCLUSIONS

Current knowledge of BLCA CAFs and the TME is being increasingly applied to improve BLCA therapy. There is a need to acquire a deeper understanding of CAF biology in BLCA.

PATIENT SUMMARY

Tumour cells are surrounded by nontumoural cells that contribute to the determination of the behaviour of cancers. Among them are cancer-associated fibroblasts. The "neighbourhoods" established through these cellular interactions can now be studied with much greater resolution. Understanding these features of tumours will contribute to the designing of more effective therapies, especially in relationship to bladder cancer immunotherapy.

Prognostic implications of necroptosis-related long noncoding RNA signatures in muscle-invasive bladder cancer

Background: Bladder cancer (BLCA) is the sixth most common cancer in men, with an increasing incidence of morbidity and mortality. Necroptosis is a type of programmed cell death and plays a critical role in the biological processes of bladder cancer (BLCA). However, current studies focusing on long noncoding RNA (lncRNA) and necroptosis in cancer are limited, and there is no research about necroptosis-related lncRNAs (NRLs) in BLCA. Methods: We obtained the RNA-seq data and corresponding clinical information of BLCA from The Cancer Genome Atlas (TCGA) database. The seven determined prognostic NLRs were analyzed by several methods and verified by RT-qPCR. Then, a risk signature was established based on the aforementioned prognostic NLRs. To identify it, we evaluated its prognostic value by Kaplan-Meier (K-M) survival curve and receiver operating characteristics (ROC) curve analysis. Moreover, the relationships between risk signature and clinical features, functional enrichment, immune landscape, and drug resistance were explored as well. Results: We constructed a signature based on seven defined NLRs (HMGA2-AS1, LINC02489, ETV7-AS1, EMSLR, AC005954.1, STAG3L5P-PVRIG2P-PILRB, and LINC02178). Patients in the low-risk cohort had longer survival times than those in the high-risk cohort, and the area under the ROC curve (AUC) value of risk signature was higher than other clinical variables. Functional analyses, the infiltrating level of immune cells and functions, ESTIMATE score, and immune checkpoint analysis all indicated that the high-risk group was in a relatively immune-activated state. In terms of treatments, patients in the high-risk group were more sensitive to immunotherapy, especially anti-PD1/PD-L1 immunotherapy and conventional chemotherapy. Conclusion: The novel NLR signature acts as an invaluable tool for predicting prognosis, immune microenvironment, and drug resistance in muscle-invasive bladder cancer (MIBC) patients.

A novel prognostic model based on cellular senescence-related gene signature for bladder cancer

Background

Cellular senescence plays crucial role in the progression of tumors. However, the expression patterns and clinical significance of cellular senescence-related genes in bladder cancer (BCa) are still not clearly clarified. This study aimed to establish a prognosis model based on senescence-related genes in BCa.

Methods

The transcriptional profile data and clinical information of BCa were downloaded from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analyses were performed to develop a prognostic model in the TCGA cohort. The GSE13507 cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate underlying mechanisms.

Results

A six-gene signature (CBX7, EPHA3, STK40, TGFB1I1, SREBF1, MYC) was constructed in the TCGA databases. Patients were classified into high risk and low risk group in terms of the median risk score. Survival analysis revealed that patients in the higher risk group presented significantly worse prognosis. Receiver operating characteristic (ROC) curve analysis verified the moderate predictive power of the risk model based on the six senescence-related genes signature. Further analysis indicated that the clinicopathological features analysis were significantly different between the two risk groups. As expected, the signature presented prognostic significance in the GSE13507 cohort. Functional analysis indicated that immune-related pathways activity, immune cell infiltration and immune-related function were different between two risk groups. In addition, risk score were positively correlated with multiple immunotherapy biomarkers.

Conclusion

Our study revealed that a novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa.

Distinct immune and inflammatory response patterns contribute to the identification of poor prognosis and advanced clinical characters in bladder cancer patients

Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy's response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

The Upregulation of Caffeic Acid Phenethyl Ester on Growth Differentiation Factor 15 Inhibits Transforming Growth Factor β/Smad Signaling in Bladder Carcinoma Cells

Growth differentiation factor 15 (GDF15) is known as a TGFβ-like cytokine acting on the TGFβ receptor to modulate target genes. GDF15 is regarded as a tumor suppressor gene in the human bladder and the caffeic acid phenethyl ester (CAPE) induces GDF15 expression to inhibit the tumor growth in vitro and in vivo. However, the interactions among GDF15, CAPE, and TGFβ/Smads signaling in the human bladder carcinoma cells remain unexplored. Results revealed that TGFβ downregulated the expression of GDF15 via the activation of Smad 2/3 and Smad 1/5. Induction of GDF15 on its downstream genes, NDRG1 and maspin, is dependent on the TGFβ/Smad pathways. Moreover, TGFβ blocked the CAPE-inducing expressions of GDF15, maspin, and NDRG1. Pretreatment of TGF receptor kinase inhibitor not only blocked the activation of TGFβ but also attenuated the activation of GDF15 on the expressions of maspin and NDRG1. The CAPE treatment attenuated the activation of TGFβ on cell proliferation and invasion. Our findings indicate that TGFβ downregulated the expressions of GDF15, maspin, and NDRG1 via TGFβ/Smad signaling. Whereas, CAPE acts as an antagonist on TGFβ/Smad signaling to block the effect of TGFβ on the GDF15 expression and cell proliferation and invasion in bladder carcinoma cells.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle–based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle–related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint–associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle–based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.