A novel anoikis-related prognostic signature associated with prognosis and immune infiltration landscape in lung adenocarcinoma.

One of the most prevalent malignancies in the world is lung adenocarcinoma (LUAD), with a large number of people dying from lung cancer each year. Anoikis has a crucial function in tumor metastasis, promoting cancer cell shedding and survival from the primary tumor site. However, the role of anoikis in LUAD is still unclear. The GeneCard database (https://www.genecards.org/) was utilized to obtain anoikis-related genes with correlation greater than 0.4. Differential analysis was employed to acquire differential genes. Univariate, multifactorial Cox analyses and the least absolute shrinkage and selection operator were then utilized to capture genes connected to overall survival time. These genes were used to build prognostic models. The predictive model was analyzed and visualized. Survival analysis was conducted on the model and risk scores were calculated. The TCGA samples were split into groups of low and high risk depending on risk scores. A Gene Expression Omnibus database sample was used for external verification. Immunization estimates were performed using ESTIMATE, CiberSort and single sample gene set enrichment analysis. The connection between the prognostic gene model and immune cells was analyzed. Drug susceptibility prediction analysis was performed. The clinical information for samples was extracted and analyzed. We selected six genes related to anoikis in LUAD to construct a prognosis model (CDC25C, ITPRIP, SLCO1B3, CDX2, CSPG4 and PIK3CG). Compared with cases of high-risk scores, the overall survival of those with low risk was significantly elevated based on Kaplan-Meier survival analysis. Immune function analysis exhibited that different risk groups had different immune states. The results of ESTIMATE, CiberSort and single sample gene set enrichment analysis showed great gaps in immunization between patients in the two groups. The normogram of the risk score and the LUAD clinicopathological features was constructed. Principal component analysis showed that this model could effectively distinguish the two groups of LUAD patients. We integrated multiple anoikis-related genes to build a prognostic model. This investigation demonstrates that anoikis-related genes can be used as a stratification element for fine therapy of individuals with LUAD.

Liu Y ，Hu S ，Teng M ，Qing Y ，Dong X ，Chen L ，Ai K ... -  《-》

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation.

Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors. Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted. We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation. The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Guo W ，Zhao G ，Liu S ，Deng T ，Zhang G ，Zhang B ... -  《-》

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Shen X ，Xie J ，Liu S ，Cai Y ，Yuan S ，Uehara Y ，Zhu D ，Zheng M ... -  《-》

Anoikis-related gene signatures can aid prognosis of lung adenocarcinoma.

Mo G ，Long X ，Hu Z ，Tang Y ，Zhou Z ... -  《Advances in Clinical and Experimental Medicine》

Identification of a novel anoikis-related gene signature to predict prognosis and tumor microenvironment in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most prevalent histotype of non-small cell lung cancer. Anoikis, an alternative form of programmed cell death, plays a pivotal role in cancer invasion and metastasis, preventing the detached cancer cells from readhering to other substrates for abnormal proliferation. The aim of this study was to conduct a comprehensive analyses of the prognostic implications of anoikis-related genes (ARGs) in LUAD. ARGs were selected from The Cancer Genome Atlas (TCGA) database and Genecards dataset using differential expression analysis. The signature incorporating ARGs was identified using univariate Cox regression analysis and LASSO regression analysis. Furthermore, a nomogram containing the signature and clinical information was developed through univariate and multivariate Cox regression analysis. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of these risk models. Finally, functional analysis of the selected ARGs in signature and analysis of immune landscape were also conducted. A 16-gene signature was integrated to stratify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and TNM stage were identified as independent prognostic factors and utilized to develop a nomogram. Both the signature and the nomogram showed satisfactory prediction performance in predicting overall survival (OS) of LUAD patients. The ARGs were enriched in several biological functions and signaling pathways. Finally, differences of immune landscape were investigated among the high- and low-risk groups stratified by the signature. This study revealed potential relationships between ARGs and prognosis of LUAD. The prognostic predictors identified in present study could be utilized as potential biomarkers for clinical applications.

Diao X ，Guo C ，Li S 《-》