Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Luo, Chenghan; Lei, Mengyuan; Zhang, Yixia; Zhang, Qian; Li, Lifeng; Lian, Jingyao; Li, Shasha; Wang, Liping; Pi, Guofu

doi:10.1111/jcmm.14719

Cited by 58 publications

(53 citation statements)

References 63 publications

(176 reference statements)

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“…Different models for predicting the prognosis of lung adenocarcinoma have been developed, based on the tumor microenvironment (Yue et al, 2019), TP53 mutation (Long et al, 2019), and tumor immunology (Luo et al, 2019). However, to our knowledge, in lung adenocarcinoma, no signature based on hypoxia-associated genes has been constructed yet.…”

Section: Introductionmentioning

confidence: 99%

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Cao

et al. 2020

Front. Genet.

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Background: A hypoxia microenvironment plays a role in the initiation and progression of many cancer types, but its involvement in lung adenocarcinoma is still unclear. This study aimed to explore the potential correlation between hypoxia and lung adenocarcinoma and establish the hypoxia-associated gene signature in lung adenocarcinoma. Methods: Lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The genes to be included in the hypoxia-associated signature were selected by performing univariate Cox regression analysis and lasso regression analysis. Then, the gene signature was verified by performing a survival analysis and constructing the multiple receiver operating characteristic (ROC) curve. The CIBERSORT tool was then used to investigate the potential correlation between the gene signature and immune cells. Moreover, a nomogram was constructed and evaluated by calculating the C-index. Results: Four genes (XPNPEP1, ANGPTL4, SLC2A1, and PFKP) were included in the final signature. The results showed that patients in the high-risk group showed worse survival than those in the low-risk group. Moreover, we found two types of immune cells (memory activated CD4 + T cell and M0 macrophages) which showed a significant infiltration in the tissues of the high-risk group patients. Conclusion: The hypoxia-associated gene signature established and validated in this study could be used as a potential prognostic factor in lung adenocarcinoma and may guide the immunotherapy choice.

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Section: Introductionmentioning

confidence: 99%

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Cao

et al. 2020

Front. Genet.

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“…Abnormal regulation of cell division and the mitotic cell cycle are key to the occurrence of cancer [25]. Moreover, many studies have shown that metabolic pathways play an important role in HCC [26,27]. In this ceRNA network, we found that 30 lncRNAs may regulate the expression of 75 mRNAs by competitively binding 16 miRNAs.…”

Section: Discussionmentioning

confidence: 81%

Screening prognosis-related lncRNAs based on WGCNA to establish a new risk score for predicting prognosis in patients with hepatocellular carcinoma

Zhou

Dou

Jiao

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods: In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in the The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso‐penalized Cox regression analysis and nomogram model were used to establish new risk scoring system and predict the prognosis of patients with liver cancer. Results: A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso‐penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Conclusions: In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC, and the establishment of new risk scoring system and nomogram model provide the new perspective for predicting the prognosis of HCC.

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“…Furthermore, our results also revealed that, the in ltration levels of macrophages, DCs, B cells, CD8+ T cells, CD4+ T cells and neutrophils were signi cantly positively correlated with ANLN expression in HCC. A recent study has recognized ANLN as an immune-related gene for lung adenocarcinoma [22]. Although few reports are available to examine the effect of ANLN on the immune microenvironment of HCC, our research provides novel clues for understanding the role of ANLN in HCC.…”

Section: Discussionmentioning

confidence: 86%

Up-regulated Expression and Related Gene Regulatory Networks of ANLN Predict Poor Prognosis and Correlate with Immune Infiltration in Hepatocellular Carcinoma

Hu¹,

Yang²,

Sang³

2020

Preprint

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Background: The aberrant Anillin (ANLN) expression is reported to be associated with carcinogenesis. In this study, sequencing data collected from the Cancer Genome Atlas database were utilized to analyze ANLN expression in hepatocellular carcinoma (HCC).Methods: The relationships of clinicopathological features with ANLN were investigated, and gene set enrichment analysis (GSEA) was performed to reveal the ANLN-related functions. LinkedOmics was employed to identify the co-expressed genes of ANLN and to examine the target networks of kinases, microRNAs (miRNAs) and transcription factors (TFs). Besides, the correlation of ANLN expression with cancer immune infiltrates was analyzed by TIMER. Results: ANLN over-expression predicted dismal prognosis, and GESA results revealed several functions that were related to cell cycle and mRNA binding. Moreover, functional network analysis indicated that, ANLN might regulate DNA replication and cell cycle signaling through pathways that involved several cancer-related kinases, miRNAs and E2F1. Additionally, ANLN was suggested to be associated with the infiltration of several immune cells, which was proved to be upregulated in both HCC cells and tissues. Conclusion: Those efficiently mined data reveal information regarding ANLN expression, the potential regulatory networks and the relationship with immune infiltration in HCC, which lay a foundation for further study on the role of ANLN in carcinogenesis.

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Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Cited by 58 publications

References 63 publications

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Screening prognosis-related lncRNAs based on WGCNA to establish a new risk score for predicting prognosis in patients with hepatocellular carcinoma

Up-regulated Expression and Related Gene Regulatory Networks of ANLN Predict Poor Prognosis and Correlate with Immune Infiltration in Hepatocellular Carcinoma

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