Meaningful nomograms based on systemic immune inflammation index predicted survival in metastatic pancreatic cancer patients receiving chemotherapy.

OBJECTIVE: The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy. METHODS: This study is retrospective and consecutively included 143 patients from January 2013 to June 2021. The receiver operating characteristic (ROC) curve with the area under the curve (AUC) is utilized to determine the optimal cut-off value. The Kaplan-Meier survival analysis, univariate and multivariable Cox regression analysis are exploited to identify the correlation of inflammatory biomarkers and clinicopathological features with survival. R software are run to construct nomograms based on independent risk factors to visualize survival. Nomogram model is examined using calibration curve and decision curve analysis (DCA). RESULTS: The best cut-off values of 966.71, 0.257, and 2.54 for the systemic immunological inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were obtained by ROC analysis. Cox proportional-hazards model revealed that baseline SII, history of drinking and metastasis sites were independent prognostic indices for survival. We established prognostic nomograms for primary endpoints of this study. The nomograms' predictive potential and clinical efficacy have been evaluated by calibration curves and DCA. CONCLUSION: We constructed nomograms based on independent prognostic factors, these models have promising applications in clinical practice to assist clinicians in personalizing the management of patients.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

BACKGROUND: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. METHODS: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. RESULTS: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. CONCLUSIONS: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Comprehensive analysis identifies cuproptosis-related gene DLAT as a potential prognostic and immunological biomarker in pancreatic adenocarcinoma.

BACKGROUND: Cuproptosis is a regulated cell death form associated with tumor progression, clinical outcomes, and immune response. However, the role of cuproptosis in pancreatic adenocarcinoma (PAAD) remains unclear. This study aims to investigate the implications of cuproptosis-related genes (CRGs) in PAAD by integrated bioinformatic methods and clinical validation. METHODS: Gene expression data and clinical information were downloaded from UCSC Xena platform. We analyzed the expression, mutation, methylation, and correlations of CRGs in PAAD. Then, based on the expression profiles of CRGs, patients were divided into 3 groups by consensus clustering algorithm. Dihydrolipoamide acetyltransferase (DLAT) was chosen for further exploration, including prognostic analysis, co-expression analysis, functional enrichment analysis, and immune landscape analysis. The DLAT-based risk model was established by Cox and LASSO regression analysis in the training cohort, and then verified in the validation cohort. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays were performed to examine the expression levels of DLAT in vitro and in vivo, respectively. RESULTS: Most CRGs were highly expressed in PAAD. Among these genes, increased DLAT could serve as an independent risk factor for survival. Co-expression network and functional enrichment analysis indicated that DLAT was engaged in multiple tumor-related pathways. Moreover, DLAT expression was positively correlated with diverse immunological characteristics, such as immune cell infiltration, cancer-immunity cycle, immunotherapy-predicted pathways, and inhibitory immune checkpoints. Submap analysis demonstrated that DLAT-high patients were more responsive to immunotherapeutic agents. Notably, the DLAT-based risk score model possessed high accuracy in predicting prognosis. Finally, the upregulated expression of DLAT was verified by RT-qPCR and IHC assays. CONCLUSIONS: We developed a DLAT-based model to predict patients' clinical outcomes and demonstrated that DLAT was a promising prognostic and immunological biomarker in PAAD, thereby providing a new possibility for tumor therapy.