Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures. Methods: RNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy. Results: Based on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice. Conclusion: The IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients.

Immunotherapy and Prevention of Cancer by Nanovaccines Loaded with Whole-Cell Components of Tumor Tissues or Cells.

Tumor tissues/cells are the best sources of antigens to prepare cancer vaccines. However, due to the difficulty of solubilization and delivery of water-insoluble antigens in tumor tissues/cells, including water-insoluble antigens into cancer vaccines and delivering such vaccines efficiently to antigen-presenting cells (APCs) remain challenging. To solve these problems, herein, water-insoluble components of tumor tissues/cells are solubilized by 8 m urea and thus whole components of micrometer-sized tumor cells are reasssembled into nanosized nanovaccines. To induce maximized immunization efficacy, various antigens are loaded both inside and on the surface of nanovaccines. By encapsulating both water-insoluble and water-soluble components of tumor tissues/cells into nanovaccines, the nanovaccines are efficiently phagocytosed by APCs and showed better therapeutic efficacy than the nanovaccine loaded with only water-soluble components in melanoma and breast cancer. Anti-PD-1 antibody and metformin can improve the efficacy of nanovaccines. In addition, the nanovaccines can prevent lung cancer (100%) and melanoma (70%) efficiently in mice. T cell analysis and tumor microenvironment analysis indicate that tumor-specific T cells are induced by nanovaccines and both adaptive and innate immune responses against cancer cells are activated by nanovaccines. Overall, this study demonstrates a universal method to make tumor-cell-based nanovaccines for cancer immunotherapy and prevention.

The prognostic value of abnormally expressed lncRNAs in prostatic carcinoma: A systematic review and meta-analysis.

BACKGROUND: Several long noncoding RNAs (lncRNAs) are abnormally expressed in prostate cancer (PCa), suggesting that they could serve as novel prognostic markers. The current meta-analysis was undertaken to better define the prognostic value of various lncRNAs in PCa. METHODS: The PubMed, Embase, Medline, and Cochrane Library databases were systematically searched up to February 19, 2017, to retrieve eligible articles. Outcomes analyzed were biochemical recurrence-free survival (BRFS), overall survival (OS), metastasis-free survival (MFS), and prostate cancer-specific survival (PCSS). Pooled hazard ratios (HRs) and 95% confidence intervals (95%CIs) were calculated using fixed-effects or random-effects models. RESULTS: A total of 10 studies, evaluating 11 PCa-related lncRNAs, were included in the meta-analysis. Pooled results indicate that the abnormal expression of candidate lncRNAs in PCa samples predicted poor BRFS (HR: 1.67, 95%CI: 1.37-2.04, P < .05), without significant heterogeneity among studies (I = 44%, P = .06). Low PCAT14 expression was negatively associated with OS (HR: 0.66, 95%CI: 0.54-0.79, P < .05), MFS (HR: 0.59, 95%CI: 0.48-0.72, P < .05), and PCSS (HR: 0.50, 95%CI: 0.38-0.66, P < .05). Again, there was no significant heterogeneity among studies. The robustness of our results was confirmed by sensitivity and publication bias analyses. CONCLUSION: We conclude that expression analysis of selected lncRNAs may be of prognostic value in PCa patients.