The predictive effect of immune therapy and chemotherapy under T cell-related gene prognostic index for Gastric cancer

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Disulfidptosis is a novel type of cell death mediated by SLC7A11-induced disulfide stress. Gastric cancer (GC) is a common malignant gastrointestinal tumor. Existing evidence shows that SLC7A11 can regulate cell death and improve the progression of GC, suggesting disulfidptosis may exist in the pathological process of GC. However, the underlying functions of disulfidptosis regulators in GC remain unknown. The dataset of GSE54129 was screened to comprehensively investigate the disulfidptosis-related diagnostic clusters and immune landscapes in GC. Totally 15 significant disulfidptosis regulators were identified via difference analysis between GC samples and controls. Then random forest model was utilized to assess their importance score (mean decrease Gini). Then a nomogram model was constructed, which could offer benefit to patients based on our subsequent decision curve analysis. All the included GC patients were divided into 2 disulfidptosis subgroups (clusterA and clusterB) according to the significant disulfidptosis regulators in virtue of consensus clustering analysis. The disulfidptosis score of each sample was calculated through PCA algorithms to quantify the disulfidptosis subtypes. Patients from clusterB exhibited lower disulfidptosis scores than those of patients in clusterA. In addition, we found that the cases in clusterB were closely associated with the immunity of activated CD4 T cell, etc., while clusterA was linked to immature dendritic cell, mast cell, natural killer T cell, natural killer cell, etc., which has a higher disulfidptosis score. Therefore, disulfidptosis regulators play an important role in the pathological process of GC, providing a promising marker and an immunotherapeutic strategy for future GC therapy.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.