Dataset Information

Angiogenic Factor-Based Signature Predicts Prognosis and Immunotherapy Response in Non-Small-Cell Lung Cancer.

ABSTRACT: Non-small-cell lung cancer (NSCLC) is one of the most common malignancies, and specific molecular targets are still lacking. Angiogenesis plays a central regulatory role in the growth and metastasis of malignant tumors and angiogenic factors (AFs) are involved. Although there are many studies comparing AFs and cancer, a prognostic risk model for AFs and cancer in humans has not been reported in the literature. This study aimed to identify the key AFs closely related to the process of NSCLC development, and four genes have been found, C1QTNF6, SLC2A1, PTX3, and FSTL3. Then, we constructed a novel prognostic risk model based on these four genes in non-small-cell lung cancer (NSCLC) and fully analyzed the relationship with clinical features, immune infiltration, genomes, and predictors. This model had good discrimination and calibration and will perform well in predicting the prognosis of treatment in clinical practice.

SUBMITTER: Gu X

PROVIDER: S-EPMC9158321 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Angiogenic Factor-Based Signature Predicts Prognosis and Immunotherapy Response in Non-Small-Cell Lung Cancer.

Gu Xinpei X Chu Liuxi L Kang Yanlan Y

Frontiers in genetics 20220518

Non-small-cell lung cancer (NSCLC) is one of the most common malignancies, and specific molecular targets are still lacking. Angiogenesis plays a central regulatory role in the growth and metastasis of malignant tumors and angiogenic factors (AFs) are involved. Although there are many studies comparing AFs and cancer, a prognostic risk model for AFs and cancer in humans has not been reported in the literature. This study aimed to identify the key AFs closely related to the process of NSCLC devel ...[more]

PMID: 35664334

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) with poor survival in advanced stage. Nowadays the rate of nonsmoking patients has dramatically increased and may be associated with the presence of driver mutations. Better understanding of the mutation profile data of nonsmoking LUAD patients are critical to predict survival and provide greater benefits to more patients. The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) has been shown to play an important role in molecular tumorigenesis of NSCLC. However, the clinical relevance of APOBEC in nonsmoking LUAD remains to be understood.MethodsLUAD patients with somatic mutation and RNA sequencing data obtained from The Cancer Genome Atlas (TCGA) were assessed and screened in the Gene Expression Omnibus. Transcriptome data and mutational signatures were analyzed using R package. Then, we used the least absolute shrinkage and selection operator (LASSO) regression model to construct the APOBEC3 score (APOBEC3 score) model. The prognostic value was evaluated using Kaplan-Meier analysis. Finally, the functional enrichment analysis of differential expressed genes (DEGs) and the immune-related features were also estimated using R package.ResultsBy analyzing the mutational profile data of NSCLC in the TCGA database, we found that different mutation patterns existed between smoking and nonsmoking patients, and the APOBEC3 family played an important role in the mutation pattern of nonsmoking patients with LUAD. We established an APOBEC3 score and found that TCW (W = A or T) mutation counts were significantly greater in the high APOBEC3 score group than in the low APOBEC3 score group. Furthermore, there were different immune feathers and prognostic values between the high and low APOBEC3 score patients, suggesting an independent prognostic factor of APOBEC3 in nonsmoking LUAD patients.ConclusionsWe established a comprehensive view of APOBEC3 mutations in nonsmoking LUAD patients. Our review provides new insights into using the APOBEC3 mutation to predict prognosis and improve the immunotherapy response for future applications.

Project description:BackgroundGastrointestinal (GI) cancers have high incidence rates and mortality rates. Anoikis is a special type of cell apoptosis, and anoikis resistance has been reported to be associated with tumor malignancy. We aimed to explore the roles of anoikis-related genes (ARGs) in the GI cancer prognosis.MethodsWe extracted RNA sequencing and clinical data from The Cancer Genome Atlas and Gene Expression Omnibu databases for patients with esophageal cancer, gastric cancer, colon cancer and rectal cancer and identified ARGs from GeneCards and Harmonizome. Anoikis-related patterns were identified via unsupervised clustering analysis. We constructed a prognostic signature (Anoscore) based on prognostic ARGs through univariate, LASSO, and multivariate Cox regression analyses. The model was validated and evaluated using Kaplan-Meier analysis, receiver operating characteristic curves, univariate Cox regression analysis, multivariate Cox regression analysis, column charts, and calibration curves. We also performed a single-cell sequencing analysis of candidate genes via TISCH2. A correlation analysis between the Anoscore, the tumor microenvironment and drug sensitivity was conducted in GI cancers. The expression and function of some candidate genes were validated in vitro.ResultsIn terms of prognostic ARGs, two anoikis-related patterns, ARG clusters A and B, were identified. ARG cluster B had a worse prognosis than did ARG cluster A. Subsequently, the Anoscore was developed as an independent prognostic factor. It demonstrated the robust predictive capability for the prognosis of patients with GI cancers. Notably, patients with high Anoscores exhibited poor outcomes. In addition, we established a nomogram (Ano-nomogram) based on the Anoscore and clinicopathological factors of patients to predict the 3-year and 5-year survival probabilities. Moreover, patients with high Anoscores had higher levels of immune cell infiltration and higher immune checkpoint expression. The drug sensitivity analysis revealed that patients with high or low Anoscores were sensitive to different chemotherapies and targeted drugs. S100A11 and TLR3, representative candidate genes, exhibited different expression patterns and biological functions.ConclusionThis study highlighted the significant potential of the Anoscore in predicting prognosis and guiding the selection of personalized therapeutic regimens for patients with GI cancers.

Project description:BackgroundLung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high annual mortality. Tsvetkov et al. recently found a new form of regulated cell death, termed cuproptosis. The prognostic value of cuproptosis-related gene signature in LUAD remains uncertain.MethodsA training cohort is identified by the TCGA-LUAD dataset, whereas validation cohorts one and two are identified by GSE72094 and GSE68465, respectively. GeneCard and GSEA were used to extract genes related to cuproptosis. Cox regression, Kaplan-Meier regression, and LASSO regression were used to construct a gene signature. The model's applicability was evaluated by Kaplan-Meier estimators, Cox models, ROC, and tAUC across two independent validation cohorts. We examined the model's connections with other forms of regulated cell death. The immunotherapy ability of the signature was demonstrated by applying TMB, immune relevant signatures, and TIDE. The GSEA and immune infiltration analysis offer a better understanding of how the signature functions and the role of immune cells in its prognostic power.ResultsA ten-gene signature was built and demonstrated owning prognostic power by being applied to the validation cohorts. The GSEA uncovered that the unfolded protein response, glycolysis/gluconeogenesis, and MYC were highly related to the gene signature. The ten-gene signature is closely related to related genes of apoptosis, necroptosis, pyroptosis, and ferroptosis. Our signature may have utility in predicting immunotherapy efficacy in LUADs. Mast cells were identified as key players that support the predicting capacity of the ten-gene signature through the immune infiltrating analysis.ConclusionsThe novel ten-gene signature associated with apoptosis in cuproptosis that we obtained may contribute to improved LUAD management strategies and the ability to predict response to LUAD immunotherapy. It is suggested that mast cell infiltration might be related to the prognostic power of this signature.

Dataset Information

Angiogenic Factor-Based Signature Predicts Prognosis and Immunotherapy Response in Non-Small-Cell Lung Cancer.

Publications

Angiogenic Factor-Based Signature Predicts Prognosis and Immunotherapy Response in Non-Small-Cell Lung Cancer.

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