Additional file 1: Table S1. Summary of Polyamine Meatabolismrelated genes. Table S2. Samples clustering in CRC RNA-seq meta cohorts. Table S3. List of 10 major signaling pathway genes associated with cancer. Table S4. ssGSEA results for immune cells of meta cohort. Table S5. Differental PAM related genes for PAMcluster. Table S6. The gene of construction PAMscore. Table S7. Samples clustering in CRC RNA-seq meta cohorts. Table S8. The gene of construction PAMscore. PAMscore subgroup analysis by constructing a PAM scoring model. Table S9. Drug susceptibility results corresponding to PAMscore model genes. Table S10. Importance results of random forest screening. Table S11. Communication of immune cell based on PAMSscore model genes.
Abstract 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
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.
Abstract Tryptophan metabolism is intricately associated with the progression of colon cancer. This research endeavored to meticulously analyze tryptophan metabolic characteristics in colon cancer and forecast immunotherapy responses. Patients were stratified into subtypes through consistent clustering, and a tryptophan metabolic risk score model was constructed using the random forest algorithm. Based on these risk scores, patients were delineated into high and low-risk groups, and their clinicopathologic characteristics, immune cell infiltration, immune checkpoint expression, and signaling pathway disparities were examined. The Oncopredict algorithm facilitated the identification of sensitive chemotherapeutic agents, while the immune escape score was employed to evaluate the immunotherapy response across risk groups. Transcriptomic sequencing findings were corroborated by single-cell sequencing from Shanghai Ruijin Hospital. Two distinct subtypes of colon cancer patients emerged, exhibiting significant prognostic and immune cell infiltration differences. The high-risk group demonstrated a poorer prognosis (p<0.001), advanced clinical stage (p<0.001), and elevated immunosuppressive cell expression (p<0.05). Additionally, three chemotherapeutic drugs showed efficacy in the high-risk cohort, which also displayed a heightened immune escape potential (p<0.05) and diminished response to immunotherapy. Single-cell sequencing validated the overexpression of tryptophan-related genes in epithelial cells. In conclusion, tryptophan metabolism significantly influences the colon cancer immune microenvironment, with high-risk patients experiencing adverse prognoses and potentially reduced efficacy of immunotherapy.
Abstract Background Changes of 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 were still 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 CRC. Methods Based on unsupervised consistent clustering and 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 characteristic 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 higher levels of immune checkpoint gene expression, implying a potential role of PAM genes in regulating response to immunotherapy. To further verify above results, we demonstrated a high-resolution landscape of TME and cell-cell communication network in different PAM patterns with 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 formation of TME and predicting the prognosis of CRC patients, providing novel strategies for immunotherapy and the targeting therapy of polyamine metabolites.
Emerging studies have shown that pyroptosis plays a non-negligible role in the development and treatment of tumors. However, the mechanism of pyroptosis in colorectal cancer (CRC) remains still unclear. Therefore, this study investigated the role of pyroptosis in CRC.A pyroptosis-related risk model was developed using univariate Cox regression and LASSO Cox regression analyses. Based on this model, pyroptosis-related risk scores (PRS) of CRC samples with OS time > 0 from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database were calculated. The abundance of immune cells in CRC tumor microenvironment (TME) was predicted by single-sample gene-set enrichment analysis (ssGSEA). Then, the responses to chemotherapy and immunotherapy were predicted by pRRophetic algorithm, the tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms, respectively. Moreover, the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset (PRISM) were used to explore novel drug treatment strategies of CRC. Finally, we investigated pyroptosis-related genes in the level of single-cell and validated the expression levels of these genes between normal and CRC cell lines by RT-qPCR.Survival analysis showed that CRC samples with low PRS had better overall survival (OS) and progression-free survival (PFS). CRC samples with low PRS had higher immune-related gene expression and immune cell infiltration than those with high PRS. Besides, CRC samples with low PRS were more likely to benefit from 5-fluorouracil based chemotherapy and anti-PD-1 immunotherapy. In novel drug prediction, some compounds such as C6-ceramide and noretynodrel, were inferred as potential drugs for CRC with different PRS. Single-cell analysis revealed pyroptosis-related genes were highly expressed in tumor cells. RT-qPCR also demonstrated different expression levels of these genes between normal and CRC cell lines.Taken together, this study provides a comprehensive investigation of the role of pyroptosis in CRC at the bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) levels, advances our understanding of CRC characteristics, and guides more effective treatment regimens.
Cetuximab (CTX) is an effective targeted drug for the treatment of metastatic colorectal cancer, but it is effective only in patients with wild-type KRAS genes. Even in this subset of patients, the sensitivity of CTX in patients with right hemi-colon cancer is much lower than that in patients with left hemi-colon cancer. This significantly limits its clinical application. Therefore, further elucidation of the underlying molecular mechanisms is needed. N-myc downstream-regulated gene 1 (NDRG1) plays an important role in solid tumor invasion and metastasis, but whether it can influence CTX sensitivity has not been thoroughly investigated.
Metabolic reprogramming provides tumors with an energy source and biofuel to support their survival in the malignant microenvironment. Extensive research into the intrinsic oncogenic mechanisms of the tumor microenvironment (TME) has established that cancer-associated fibroblast (CAFs) and metabolic reprogramming regulates tumor progression through numerous biological activities, including tumor immunosuppression, chronic inflammation, and ecological niche remodeling. Specifically, immunosuppressive TME formation is promoted and mediators released via CAFs and multiple immune cells that collectively support chronic inflammation, thereby inducing pre-metastatic ecological niche formation, and ultimately driving a vicious cycle of tumor proliferation and metastasis. This review comprehensively explores the process of CAFs and metabolic regulation of the dynamic evolution of tumor-adapted TME, with particular focus on the mechanisms by which CAFs promote the formation of an immunosuppressive microenvironment and support metastasis. Existing findings confirm that multiple components of the TME act cooperatively to accelerate the progression of tumor events. The potential applications and challenges of targeted therapies based on CAFs in the clinical setting are further discussed in the context of advancing research related to CAFs.
Additional file 2: Figure S1.Boxplot of the expression of PAM genes in tumor and normal sample on the TCGA cohort.Heatmap of significant different PAM genes in tumor and normal sample..GO enrichment analysis for PAM genes.KEGG enrichment analysis for PAM genes.Forest plot of prognostic gene with Univariate cox regression analysis.Protein–protein interactionfor PAM-related genes.Venn diagram showing PAM genes after intersection of datasets. Figure S2. Survival prognostic analysis of each PAM gene with high and low expression using Kaplan–Meier analysis. Figure S3.Consensus clustering of 55 PAM genes matrix for k = 3 of 1224 patients in the TCGA cohort combine the GEO cohort.Determine the relevant CDF curve and Tracking plot of Consensus clustering. Figure S4.Consensus clustering of 328 PAM prognostic genes in the meta dataset.GO enrichment analysis and KEGG enrichment analysis of 328 PAM prognostic genes.Expression heatmap of 328 PAM prognostic genes in geneCluster A and B subgroups.Forest plots for univariate and multivariate cox analysis of PAMscore. Figure S5. Survival analysis of high and low score subgroups of PAMscore for different genders, different T, N, M stages and AJCC stages in CRC patients. Figure S6.RNA expression of ACAT2, SPHK1, SNED1, KPNA2, BZW2 and KIF15 in tumor and normal tissues in TCGA dataset.IHC cell staining intensity in normal and tumor tissues of the CRC cohort. Figure S7.Expression levels of marker genes in the 6 cell types.Expression levels of marker genes in high and low cell groups.
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