BackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.MethodsThe expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.ResultsIn the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.
The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.
Background The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC). Methods The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) database were set as validation datasets. Prognostic genes were screened by using univariate cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cibersort and Estimate algorithms were used to characterize tumor immune infiltrating patterns. TIDE algorithm was used to predict immunotherapy responsiveness. Results A prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate cox regression analysis demonstrated that this signature had an independent prognostic value. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve at 1-year, 3-year, and 5-year of survival were 0.724, 0.702, and 0.776 respectively. We further demonstrated that our signature has better prognostic performance than the recently published ones and is superior to traditional clinical factors such as grade and TNM stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group. Conclusions We constructed and validated an optimal prognostic model of independent prognostic value. This five-gene signature could predict immune infiltration characteristics. The signature helps to stratify PDAC patients according to the responsiveness to immunotherapy.
IntroductionThe conflict between cancer cells and the host immune system shapes the immune tumour microenvironment (TME) in hepatocellular carcinoma (HCC). A deep understanding of the heterogeneity and intercellular communication network in the TME of HCC will provide promising strategies to orchestrate the immune system to target and eradicate cancers.MethodsHere, we performed single-cell RNA sequencing (scRNA-seq) and computational analysis of 35786 unselected single cells from 3 human HCC tumour and 3 matched adjacent samples to elucidate the heterogeneity and intercellular communication network of the TME. The specific lysis of HCC cell lines was examined in vitro using cytotoxicity assays. Granzyme B concentration in supernatants of cytotoxicity assays was measured by ELISA.ResultsWe found that VCAN+ tumour-associated macrophages (TAMs) might undergo M2-like polarization and differentiate in the tumour region. Regulatory dendritic cells (DCs) exhibited immune regulatory and tolerogenic phenotypes in the TME. Furthermore, we observed intensive potential intercellular crosstalk among C1QC+ TAMs, regulatory DCs, regulator T (Treg) cells, and exhausted CD8+ T cells that fostered an immunosuppressive niche in the HCC TME. Moreover, we identified that the TIGIT-PVR/PVRL2 axis provides a prominent coinhibitory signal in the immunosuppressive TME. In vitro, antibody blockade of PVR or PVRL2 on HCC cell lines or TIGIT blockade on immune cells increased immune cell-mediated lysis of tumour cell. This enhanced immune response is paralleled by the increased secretion of Granzyme B by immune cells.DiscussionCollectively, our study revealed the functional state, clinical significance, and intercellular communication of immunosuppressive cells in HCC at single-cell resolution. Moreover, PVR/PVRL2, interact with TIGIT act as prominent coinhibitory signals and might represent a promising, efficacious immunotherapy strategy in HCC.
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