The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Jia HR ，Li WC ，Wu L 《-》

Bioinformatics analysis of an immunotherapy responsiveness-related gene signature in predicting lung adenocarcinoma prognosis.

Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses. Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established. A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively. The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.

Jiang Y ，Hammad B ，Huang H ，Zhang C ，Xiao B ，Liu L ，Liu Q ，Liang H ，Zhao Z ，Gao Y ... -  《-》

Prognostic value of cancer-associated fibroblasts-related genes in lung adenocarcinoma.

The incidence of lung adenocarcinoma is in the forefront of malignant tumors in the world. The purpose of this study was to investigate the role of cancer-associated fibroblast-related genes (CAFRGs) in the occurrence, diagnosis and development of lung adenocarcinoma. RNA data and corresponding clinical information of lung adenocarcinoma patients were acquired from The Cancer Genome Atlas (TCGA) database. Consensus clustering was performed to identify different molecular subgroups. The tumor immune states of different subgroups were determined by Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE; https://bioinformatics.mdanderson.org/estimate/index.html), microenvironment cell populations (MCP)-counter (which can reliably quantify the abundance of eight immune cell populations and two stromal cell populations), and single sample gene set enrichment analysis (ssGSEA) analyses. In order to elucidate the potential mechanism of CAFRGs, functional enrichment analysis including gene ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG), and GSEA analysis were performed on the differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to construct the prognostic risk model, which was verified by lung adenocarcinoma data from Gene Expression Omnibus (GEO) dataset GSE37745. This study identified two molecular subgroups with significant differences in survival. High immunoscore and immune cell infiltration were more common in the subgroup with better prognosis. GO and KEGG analysis showed that DEGs between the two different subgroups were mainly concentrated in the mitotic cell cycle, cell proliferation, vascular development, and humoral immune response, adaptive immune-related pathways. GSEA analysis indicated that RNA degradation and P53 signaling pathway might be related to the increased invasiveness of lung adenocarcinoma. Risk models based on CAFRGs have demonstrated potent potential for predicting lung adenocarcinoma survival and have been validated in validation cohorts. The nomogram combined with risk model and clinical characteristics can predict the prognosis of patients with lung adenocarcinoma. The expression of CAFRGs is related to tumor immune microenvironment (TIME) of lung adenocarcinoma patients, and can predict the prognosis of lung adenocarcinoma patients.

Li W ，Shi S 《-》

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD. Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed. LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients. mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Zhang H ，Cao C ，Xiong H 《-》

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay.

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

Liang H ，Li Y ，Qu Y ，Zhang L ... -  《-》