Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma.

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear. Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database. Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk. Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Li Z ，Ma Z ，Xue H ，Shen R ，Qin K ，Zhang Y ，Zheng X ，Zhang G ... -  《Frontiers in Genetics》

A Four-Gene Prognostic Signature Based on the TEAD4 Differential Expression Predicts Overall Survival and Immune Microenvironment Estimation in Lung Adenocarcinoma.

Background: TEA domain transcription factor 4 (TEAD4) is a member of the transcriptional enhancer factor (TEF) family of transcription factors, which is studied to be linked to the tumorigenesis and progression of various forms of cancers, including lung adenocarcinoma (LUAD). However, the specific function of this gene in the progression of LUAD remains to be explored. Method: A total of 19 genes related to the Hippo pathway were analyzed to identify the significant genes involved in LUAD progression. The TCGA-LUAD data (n = 585) from public databases were mined, and the differentially expressed genes (DEGs) in patients with the differential level of TEAD4 were identified. The univariate Cox regression, zero LASSO regression coefficients, and multivariate Cox regression were performed to identify the independent prognostic signatures. The immune microenvironment estimation in the two subgroups, including immune cell infiltration, HLA family genes, and immune checkpoint genes, was assessed. The Gene Set Enrichment Analysis (GSEA) and GO were conducted to analyze the functional enrichment of DEGs between the two risk groups. The potential drugs for the high-risk subtypes were forecasted via the mode of action (moa) module of the connectivity map (CMap) database. Results: TEAD4 was found to be significantly correlated with poor prognosis in LUAD-patients. A total of 102 DEGs in TEAD4-high vs. TEAD4-low groups were identified. Among these DEGs, four genes (CPS1, ANLN, RHOV, and KRT6A) were identified as the independent prognostic signature to conduct the Cox risk model. The immune microenvironment estimation indicated a strong relationship between the high TEAD4 expression and immunotherapeutic resistance. The GSEA and GO showed that pathways, including cell cycle regulation, were enriched in the high-risk group, while immune response-related and metabolism biological processes were enriched in the low-risk group. Several small molecular perturbagens targeting CFTR or PLA2G1B, by the mode of action (moa) modules of the glucocorticoid receptor agonist, cyclooxygenase inhibitor, and NFkB pathway inhibitor, were predicted to be suited for the high-risk subtypes based on the high TEAD4 expression. Conclusion: The current study revealed TEAD4 is an immune regulation-related predictor of prognosis and a novel therapeutic target for LUAD.

Gong X ，Li N ，Sun C ，Li Z ，Xie H ... -  《-》

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low. All data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan-Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level. Through univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level. The immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

Li Y ，Shen R ，Wang A ，Zhao J ，Zhou J ，Zhang W ，Zhang R ，Zhu J ，Liu Z ，Huang JA ... -  《Frontiers in Cell and Developmental Biology》

A new prognostic model for RHOV, ABCC2, and CYP4B1 to predict the prognosis and association with immune infiltration of lung adenocarcinoma.

Lymph node metastasis is one of the important factors affecting the prognosis of lung adenocarcinoma (LUAD) patients. The key molecules in lymph node metastasis have not yet been fully revealed. Therefore, we aimed to construct a prognostic model based on lymph node metastasis-related genes to evaluate the prognosis of LUAD patients. The differentially expressed genes (DEGs) in the process of LUAD metastasis were identified in The Cancer Genome Atlas (TCGA) database, and the biological roles of the DEGs were depicted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and a protein-protein interaction (PPI) network. Survival analysis and Cox regression analysis were used to identify the genes related to the prognosis of patients with LUAD, and a nomogram and a prognostic model were constructed. The potential prognostic value, immune escape, and regulatory mechanisms of the prognostic model in LUAD progression were explored through survival analysis and gene set enrichment analysis (GSEA). A total of 75 genes were upregulated, and 138 genes were downregulated in tissues of lymph node metastasis. The expression levels of STC1, CYP17A1, RHOV, GUCA2B, TM4SF20, DEFB1, CRHR2, ABCC2, CYP4B1, KRT16, and NTS were revealed as risk factors for a poor prognosis in LUAD patients. High-risk LUAD patients had a poor prognosis in the prognostic model based on RHOV, ABCC2, and CYP4B1. The clinical stage and the risk score were found to be independent risk factors for a poor prognosis in LUAD patients, and the risk score was associated with the tumor purity, T cell, natural killer (NK) cell, and other immune cells. The prognostic model might affect the progression of LUAD using DNA replication, the cell cycle, P53, and other signaling pathways. Lymph node metastasis-related genes RHOV, ABCC2, and CYP4B1 are associated with a poor prognosis in LUAD. A prognostic model based on RHOV, ABCC2, and CYP4B1 might predict the prognosis of LUAD patients and be associated with immune infiltration.

Li Q ，Liu XL ，Jiang N ，Li QY ，Song YX ，Ke XX ，Han H ，Luo Q ，Guo Q ，Luo XY ，Chen C ... -  《-》

A novel anoikis-related gene signature to predict the prognosis, immune infiltration, and therapeutic outcome of lung adenocarcinoma.

Lung cancer is a highly aggressive disease and the leading cause of cancer-related deaths. Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. As a type of programmed cell death, anoikis serves a key role in tumor metastasis. However, as few studies have focused on anoikis and prognostic indicators in LUAD, in this study, we constructed an anoikis-related risk model to explore how anoikis could influence the tumor microenvironment (TME), clinical treatment, and prognosis in LUAD patients; we aimed to provide new insight for future research. Using patient data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized the 'limma' package to select differentially expressed genes (DEGs) associated with anoikis and then they were divided into 2 clusters with consensus clustering. Risk models were constructed with least absolute shrinkage and selection operator (LASSO) Cox regression (LCR). Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves were performed to assess the independent risk factors for different clinical characteristics, including age, sex, disease stage, grade, and their associated risk scores. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore the biological pathways in our model. The effectiveness of clinical treatment was detected according to tumor immune dysfunction and exclusion (TIDE), The Cancer Immunome Atlas (TCIA), and IMvigor210. Our model was found to divide LUAD patients into high- and low-risk groups well, in which high risk groups had poor overall survival (OS), indicating that risk score could be an independent risk factor to predict the prognosis of LUAD patients. Interestingly, we found that anoikis could not only influence the extracellular organization but also play great roles in immune infiltration and immunotherapy, which might provide a new insight for future research. The risk model constructed in this study can benefit to predict patient survival. Our results provided new potential treatment strategies.

Wang Y ，Xie C ，Su Y 《-》