Identification of Clinical and Tumor Microenvironment Characteristics of Hypoxia-Related Risk Signature in Lung Adenocarcinoma

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Clinical significance and immune landscape of a novel immune cell infiltration-based prognostic model in lung adenocarcinoma

Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients.

Construction and validation of a T cell proliferation regulator-related signature for predicting prognosis and immunotherapy response in lung adenocarcinoma

Background

As the main executor of immunotherapy, T cells significantly affect the efficacy of immunotherapy. However, the contribution of the T cell proliferation regulator to the prognosis of lung adenocarcinoma (LUAD) and immunotherapy is still unclear.

Methods

Based on T cell proliferation regulators, LUAD samples from The Cancer Genome Atlas (TCGA) were divided into two different clusters by consensus clustering. Subsequently, the T cell proliferation regulator (TPR) signature was constructed according to the prognostic T cell proliferation regulators. Combined with clinical information, a nomogram for clinical practice was constructed. The predictive ability of the signature was verified by the additional Gene Expression Omnibus (GEO) dataset. We also analyzed the differences of tumor microenvironment (TME) in different subgroups and predicted the response to immunotherapy according to the TIDE algorithm. Finally, we further explored the role of ADA (Adenosine deaminase) in the lung adenocarcinoma cell lines through the knockdown of ADA.

Results

According to the consensus clustering, there were differences in survival and tumor microenvironment between two different molecular subtypes. T cell proliferation regulator-related signature could accurately predict the prognosis of LUAD. The low-risk group had a higher level of immune infiltration and more abundant immune-related pathways, and its response to immunotherapy was significantly better than the high-risk group (Chi-square test, p<0.0001). The knockdown of ADA inhibited proliferation, migration, and invasion in lung adenocarcinoma cell lines.

Conclusion

T cell proliferation regulators were closely related to the prognosis and tumor microenvironment of LUAD patients. And the signature could well predict the prognosis of LUAD patients and their response to immunotherapy. ADA may become a new target for the treatment of LUAD.

Role of the Hypoxic-Secretome in Seed and Soil Metastatic Preparation

During tumor growth, the delivery of oxygen to cells is impaired due to aberrant or absent vasculature. This causes an adaptative response that activates the expression of genes that control several essential processes, such as glycolysis, neovascularization, immune suppression, and the cancer stemness phenotype, leading to increased metastasis and resistance to therapy. Hypoxic tumor cells also respond to an altered hypoxic microenvironment by secreting vesicles, factors, cytokines and nucleic acids that modify not only the immediate microenvironment but also organs at distant sites, allowing or facilitating the attachment and growth of tumor cells and contributing to metastasis. Hypoxia induces the release of molecules of different biochemical natures, either secreted or inside extracellular vesicles, and both tumor cells and stromal cells are involved in this process. The mechanisms by which these signals that can modify the premetastatic niche are sent from the primary tumor site include changes in the extracellular matrix, recruitment and activation of different stromal cells and immune or nonimmune cells, metabolic reprogramming, and molecular signaling network rewiring. In this review, we will discuss how hypoxia might alter the premetastatic niche through different signaling molecules.

Prognostic Modeling of Lung Adenocarcinoma Based on Hypoxia and Ferroptosis-Related Genes

Background. It is well known that hypoxia and ferroptosis are intimately connected with tumor development. The purpose of this investigation was to identify whether they have a prognostic signature. To this end, genes related to hypoxia and ferroptosis scores were investigated using bioinformatics analysis to stratify the risk of lung adenocarcinoma. Methods. Hypoxia and ferroptosis scores were estimated using The Cancer Genome Atlas (TCGA) database-derived cohort transcriptome profiles via the single sample gene set enrichment analysis (ssGSEA) algorithm. The candidate genes associated with hypoxia and ferroptosis scores were identified using weighted correlation network analysis (WGCNA) and differential expression analysis. The prognostic genes in this study were discovered using the Cox regression (CR) model in conjunction with the LASSO method, which was then utilized to create a prognostic signature. The efficacy, accuracy, and clinical value of the prognostic model were evaluated using an independent validation cohort, Receiver Operator Characteristic (ROC) curve, and nomogram. The analysis of function and immune cell infiltration was also carried out. Results. Here, we appraised 152 candidate genes expressed not the same, which were related to hypoxia and ferroptosis for prognostic modeling in The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort, and these genes were further validated in the GSE31210 cohort. We found that the 14-gene-based prognostic model, utilizing MAPK4, TNS4, WFDC2, FSTL3, ITGA2, KLK11, PHLDB2, VGLL3, SNX30, KCNQ3, SMAD9, ANGPTL4, LAMA3, and STK32A, performed well in predicting the prognosis in lung adenocarcinoma. ROC and nomogram analyses showed that risk scores based on prognostic signatures provided desirable predictive accuracy and clinical utility. Moreover, gene set variance analysis showed differential enrichment of 33 hallmark gene sets between different risk groups. Additionally, our results indicated that a higher risk score will lead to more fibroblasts and activated CD4 T  cells but fewer myeloid dendritic cells, endothelial cells, eosinophils, immature dendritic cells, and neutrophils. Conclusion. Our research found a 14-gene signature and established a nomogram that accurately predicted the prognosis in patients with lung adenocarcinoma. Clinical decision-making and therapeutic customization may benefit from these results, which may serve as a valuable reference in the future.