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.

A potential biomarker of esophageal squamous cell carcinoma WTAP promotes the proliferation and migration of ESCC.

This study focuses on the function of WTAP in esophageal squamous cell carcinoma (ESCC) samples and cell lines. The results showed that WTAP expression in ESCC tissues was significantly upregulated in 78.1% (57 of 73) of the ESCC tissues at the protein level compared with adjacent non-cancerous tissues via immunohistochemical staining. The WTAP protein expression level was positively correlated with the lymph node metastasis and TNM stage, and patients with higher WTAP protein expression level exhibited a shorter overall survival interval. Knocking down WTAP significantly reduced cell proliferation and migration but promoted cell apoptosis of TE-1and KYSE150 cells. Moreover, WTAP inhibition reduced the expression of ki67 and Snail related to cell proliferation and migration but increased the expression of Bax and Caspase-3 which were involved in cell apoptosis. In conclusion, our results suggest that the WTAP, a potential biomarker of ESCC, maybe play an important role in ESCC-genesis through regulating expression of genes related to cell proliferation, migration and apoptosis.

Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation. Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach. Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses. Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment.

Cystatin SN promotes epithelial-mesenchymal transition and serves as a prognostic biomarker in lung adenocarcinoma.

BACKGROUND: Cystatins are a class of proteins that can inhibit cysteine protease and are widely distributed in human bodily fluids and secretions. Cystatin SN (CST1), a member of the CST superfamily, is abnormally expressed in a variety of tumors. However, its effect on the occurrence and development of lung adenocarcinoma (LUAD) remains unclear. METHODS: We obtained transcriptome analysis data of CST1 from The Cancer Genome Atlas (TCGA) and GSE31210 databases. The association of CST1 expression with prognosis, gene mutations and tumor immune microenvironment was analyzed using public databases. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the potential mechanisms of CST1. RESULTS: In this study, we found that CST1 was highly expressed in lung adenocarcinoma and was associated with prognosis and tumor immune microenvironment. Genetic mutations of CST1 were shown to be related to disease-free survival (DFS) by using the c-BioPortal tool. Potential proteins binding to CST1 were identified by constructing a protein-protein interaction (PPI) network. Gene set enrichment analysis (GSEA) of CST1 revealed that CST1 was notably enriched in epithelial-mesenchymal transition (EMT). Cell experiments confirmed that overexpression of CST1 promoted lung adenocarcinoma cells migration and invasion, while knockdown of CST1 significantly inhibited lung adenocarcinoma cells migration and invasion. CONCLUSIONS: Our comprehensive bioinformatics analyses revealed that CST1 may be a novel prognostic biomarker in LUAD. Experiments confirmed that CST1 promotes epithelial-mesenchymal transition in LUAD cells. These findings will help to better understand the distinct role of CST1 in LUAD.