Immune checkpoint inhibitors (ICIs) targeting PD-1 or PD-L1 have emerged as a revolutionary treatment strategy for human cancer patients. However, as the response rate to ICI therapy varies widely among different types of tumours, we are beginning to gain insight into the mechanisms as well as biomarkers of therapeutic response and resistance. Numerous studies have highlighted the dominant role of cytotoxic T cells in determining the treatment response to ICIs. Empowered by recent technical advances, such as single-cell sequencing, tumour-infiltrating B cells have been identified as a key regulator in several solid tumours by affecting tumour progression and the response to ICIs. In the current review, we summarized recent advances regarding the role and underlying mechanisms of B cells in human cancer and therapy. Some studies have shown that B-cell abundance in cancer is positively associated with favourable clinical outcomes, while others have indicated that they are tumour-promoting, implying that the biological function of B cells is a complex landscape. The molecular mechanisms involved multiple aspects of the functions of B cells, including the activation of CD8+ T cells, the secretion of antibodies and cytokines, and the facilitation of the antigen presentation process. In addition, other crucial mechanisms, such as the functions of regulatory B cells (Bregs) and plasma cells, are discussed. Here, by summarizing the advances and dilemmas of recent studies, we depicted the current landscape of B cells in cancers and paved the way for future research in this field. Graphical Abstract

Background: Changes in Polyamine metabolism (PAM) have been shown to establish a suppressive tumor microenvironment (TME) and substantially influence the progression of cancer in the recent studies. However, newly emerging data have still been unable to fully illuminate the specific effects of PAM in human cancers. Here, we analyzed the expression profiles and clinical relevance of PAM genes in colorectal cancer (CRC). Methods: Based on unsupervised consensus clustering and principal component analysis (PCA) algorithm, we designed a scoring model to evaluate the prognosis of CRC patients and characterize the TME immune profiles, with related independent immunohistochemical validation cohort. Through comparative profiling of cell communities defined by single cell sequencing data, we identified the distinct characteristics of polyamine metabolism in the TME of CRC. Results: Three PAM patterns with distinct prognosis and TME features were recognized from 1224 CRC samples. Moreover, CRC patients could be divided into high- and low-PAMscore subgroups by PCA-based scoring system. High PAMscore subgroup were associated to more advanced stage, higher infiltration level of immunosuppressive cells, and unfavorable prognosis. These results were also validated in CRC samples from other public CRC datasets and our own cohort, which suggested PAM genes were ideal biomarkers for predicting CRC prognosis. Notably, PAMscore also corelated with microsatellite instability-high (MSI-H) status, higher tumor mutational burden (TMB), and increased immune checkpoint gene expression, implying a potential role of PAM genes in regulating response to immunotherapy. To further confirm above results, we demonstrated a high-resolution landscape of TME and cell–cell communication network in different PAM patterns using single cell sequencing data and found that polyamine metabolism affected the communication between cancer cells and several immune cells such as T cells, B cells and myeloid cells. Conclusion: In total, our findings highlighted the significance of polyamine metabolism in shaping the TME and predicting the prognosis of CRC patients, providing novel strategies for immunotherapy and the targeting polyamine metabolites.

Colorectal cancer (CRC) is one of most common tumors worldwide, causing a prominent global health burden. Cell senescence is a complex physiological state, characterized by proliferation arrest. Here, we investigated the role of cellular senescence in the heterogeneity of CRC. Based on senescence-associated genes, CRC samples were classified into different senescence patterns with different survival, cancer-related biological processes and immune cell infiltrations. A senescence-related model was then developed to calculate the senescence-related score to comprehensively explore the heterogeneity of each CRC sample such as stromal activities, immunoreactivities and drug sensitivity. Single-cell analysis revealed there were different immune cell infiltrations between low and high senescence-related model genes enrichment groups, which was confirmed by multiplex immunofluorescence staining. Pseudotime analysis indicated model genes play a pivotal role in the evolution of B cells. Besides, intercellular communication modeled by NicheNet showed tumor cells with higher enrichment of senescence-related model genes highly expressed CXCL2/3 and CCL3/4, which attracted immunosuppressive cell infiltration and promoted tumor metastasis. Finally, top 6 hub genes were identified from senescence-related model genes by PPI analysis. And RT-qPCR revealed the expression differences of hub genes between normal and CRC cell lines, indicating to some extent the clinical practicability of senescence-related model. To sum up, our study explores the impact of cellular senescence on the prognosis, TME and treatment of CRC based on senescence patterns. This provides a new perspective for CRC treatment.

Introduction: Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy.Methods: Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4⁺ T cells.Results: Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4⁺ T cells and a negative correlation between ADAMTS13 and CD4⁺ T cells.Discussion: These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.