A Signature Based on Costimulatory Molecules for the Assessment of Prognosis and Immune Characteristics in Patients With Stomach Adenocarcinoma

Identification and validation of a costimulatory molecule-related signature to predict the prognosis for uveal melanoma patients

Uveal melanoma (UVM) is the most common primary tumor in adult human eyes. Costimulatory molecules (CMs) are important in maintaining T cell biological functions and regulating immune responses. To investigate the role of CMs in UVM and exploit prognostic signature by bioinformatics analysis. This study aimed to identify and validate a CMs associated signature and investigate its role in the progression and prognosis of UVM. The expression profile data of training cohort and validation cohort were downloaded from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) dataset. 60 CM genes were identified, and 34 genes were associated with prognosis by univariate Cox regression. A prognostic signature was established with six CM genes. Further, high- and low-risk groups were divided by the median, and Kaplan-Meier (K-M) curves indicated that high-risk patients presented a poorer prognosis. We analyzed the correlation of gender, age, stage, and risk score on prognosis by univariate and multivariate regression analysis. We found that risk score was the only risk factor for prognosis. Through the integration of the tumor immune microenvironment (TIME), it was found that the high-risk group presented more immune cell infiltration and expression of immune checkpoints and obtained higher immune scores. Enrichment analysis of the biological functions of the two groups revealed that the differential parts were mainly related to cell-cell adhesion, regulation of T-cell activation, and cytokine-cytokine receptor interaction. No differences in tumor mutation burden (TMB) were found between the two groups. GNA11 and BAP1 have higher mutation frequencies in high-risk patients. Finally, based on the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) dataset, drug sensitivity analysis found that high-risk patients may be potential beneficiaries of the treatment of crizotinib or temozolomide. Taken together, our CM-related prognostic signature is a reliable biomarker that may provide ideas for future treatments for the disease.

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment. Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature. Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results. Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.