Multi-Omics Pan-Cancer Analysis of Procollagen N-Propeptidase Gene Family of ADAMTS as Novel Biomarkers to Associate with Prognosis, Tumor Immune Microenvironment, Signaling Pathways, and Drug Sensitivities.

BACKGROUND: The extracellular matrix (ECM) modeling induced by the metalloproteinases is a vital characteristic for tumor progression. Previous studies mainly focus on the functions of two subgroups of metalloproteinases: matrix metalloproteinases (MMPs) and a disintegrin and metalloproteases (ADAMs) in tumors. The roles of another important group: the ADAMs with thrombospondin motifs (ADAMTS) remain unclear. This study aimed to perform a pan-cancer analysis of procollagen N-propeptidase subgroup of ADAMTS (PNPSA). METHODS: We systematically analyzed expression landscape, genomic variations, prognostic value, and cell expression clusters of PNPSA in pan-cancer based on the multiple integrated open databases. Besides, we also analyzed the impacts of expressions and genomic variations of PNPSA members on tumor immune microenvironment (TIME) and immune-related molecules in pan-cancer based on the immune-related open databases. The Gene Set Variation Analysis (GSVA) was performed to evaluate the associations of the whole PNPSA with prognosis, tumor indicators, TIME, and drug sensitivities. Meanwhile, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to reveal related signaling pathways. Finally, immunohistochemical staining was used to validate the differential analysis results. RESULTS: We found a dual prognostic role of PNPSA members in pan-cancer and they were significantly correlated with TIME and immune-related molecules. Interestingly, the copy number variations (CNVs) of all PNPSA members were revealed to be negatively correlated with NK cell infiltration in most cancers. Single-cell sequencing analysis reveals expressions of PNPSA gene family members on some specific tumor and immune cells in addition to the fibroblasts. The GSVA score was found to have some predictive value for survival status in Brain Lower Grade Glioma (LGG), Mesothelioma (MESO), and Uveal Melanoma (UVM) and to be significantly correlated with tumorigenesis-related pathways such as PI3K-Akt, AGE-RAGE, etc. The GSVA score also shows some predictive value for chemotherapy and immunotherapy efficacy in some tumors. CONCLUSIONS: PNPSA was correlated with tumor development and might be potential tumor biomarker and therapeutic target.

The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma.

PURPOSE: Necroptosis plays an important role in the tumorigenesis, development, metastasis, and drug resistance of malignant tumors. This study explored the new model for assessing stomach adenocarcinoma (STAD) prognosis and immunotherapy by combining long noncoding RNAs associated with necroptosis. METHODS: Patient clinical data and STAD gene expression profiles were curated from The Cancer Genome Atlas (TCGA). Immune-related genes were sourced from a specialized molecular database. Perl software and R software were used for data processing and analysis. Necroptosis-related lncRNAs in STAD were pinpointed via R's correlation algorithms. These lncRNAs, in conjunction with clinical data, informed the construction of a prognostic lncRNA-associated risk score model using univariate and multivariate Cox regression analyses. The model's prognostic capacity was evaluated by Kaplan-Meier survival curves and validated as an independent prognostic variable. Further, a nomogram incorporating this model with clinical parameters was developed, offering refined individual survival predictions. Subsequent analyses of immune infiltration and chemosensitivity within necroptosis-related lncRNA clusters utilized an arsenal of bioinformatic tools, culminating in RT-PCR validation of lncRNA expression. RESULTS: Through rigorous Cox regression, 21 lncRNAs were implicated in the risk score model. Stratification by median risk scores delineated patients into high- and low-risk cohorts, with the latter demonstrating superior prognostic outcomes. The risk model was corroborated as an independent prognostic indicator for STAD. The integrative nomogram displayed high concordance between predicted and observed survival rates, as evidenced by calibration curves. Differential immune infiltration in risk-defined groups was illuminated by the single sample GSEA (ssGSEA), indicating pronounced immune presence in higher-risk patients. Tumor microenvironment (TME) analysis showed that cluster-C3 had the highest score in the analysis of the three TMEs. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters. Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters. Risk score gene expression identification results showed that these lncRNAs were abnormally expressed in gastric cancer cell lines. CONCLUSIONS: This investigation provides a robust methodological advance in prognosticating and personalizing immunotherapy for STAD, leveraging quantitatively derived tumor cluster risk scores. It posits the use of necroptosis-related lncRNAs as pivotal molecular beacons for guiding therapeutic strategies and enhancing clinical outcomes in STAD.