Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis

Zhuang, Gaojian; Zeng, Yu; Tang, Qun; He, Qian; Luo, Guoqing

doi:10.3389/fgene.2020.591079

Cited by 16 publications

(16 citation statements)

References 39 publications

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“…Together, these findings show that COL6A6 may play a pivotal role in the tumor immune microenvironment of LUAD. Gene signatures have been widely used as prognostic predictors in various cancers (45,46). Zhuang related genes and identified a four-gene signature for predicting thyroid cancer prognosis (45).…”

Section: Discussionmentioning

confidence: 99%

“…Gene signatures have been widely used as prognostic predictors in various cancers (45,46). Zhuang related genes and identified a four-gene signature for predicting thyroid cancer prognosis (45). Li et al analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts and identified a prognostic immune-related gene signature involving 25 gene pairs (46).…”

Section: Discussionmentioning

confidence: 99%

“…Gene signatures have been widely used as prognostic predictors in various cancers ( 45 , 46 ). Zhuang et al .…”

Section: Discussionmentioning

confidence: 99%

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Comprehensive Analysis of the Immune and Prognostic Implication of COL6A6 in Lung Adenocarcinoma

Qiu

Guo

et al. 2021

Front. Oncol.

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Collagen type VI alpha 6 chain (COL6A6), a novel collagen, has been considered as a tumor suppressor and therapeutic target in several tumors. However, the functional role of COL6A6 in immune cell infiltration and prognostic value in lung adenocarcinoma (LUAD) remains unknown. Here, we evaluated COL6A6 expression and its impact on survival among LUAD patients from The Cancer Genome Atlas (TCGA) and several other databases. COL6A6 was downregulated in LUAD tissues compared to normal tissues at both mRNA and protein levels. COL6A6 expression was negatively associated with pathological stage, tumor stage, and lymph node metastasis. High COL6A6 expression was a favorable prognostic factor in LUAD. Next, we explored the associations between COL6A6 expression and immune cell infiltration. COL6A6 expression was positively associated with the infiltration of B cells, T cells, neutrophils and dendritic cells. Additionally, the immune cell infiltration levels were associated with COL6A6 gene copy number in LUAD. Consistently, gene set enrichment analysis showed that various immune pathways were enriched in the LUAD samples with high COL6A6 expression, including pathways related to T cell activation and T cell receptor signaling. The impacts of COL6A6 on immune activity were further assessed by enrichment analysis of 50 COL6A6-associated immunomodulators. Thereafter, using Cox regression, we identified a seven-gene risk prediction signature based on the COL6A6-associated immunomodulators. The resulting risk score was an independent prognostic predictor in LUAD. Receiver operating characteristic curve analysis confirmed that the seven-gene signature had good prognostic accuracy in the TCGA-LUAD cohort and a Gene Expression Omnibus dataset. Finally, we constructed a clinical nomogram to predict long-term survival probabilities, and calibration curves verified its accuracy. Our findings highlight that COL6A6 is involved in tumor immunity, suggesting COL6A6 may be a potential immunotherapeutic target in LUAD. The proposed seven-gene signature is a promising prognostic biomarker in LUAD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Comprehensive Analysis of the Immune and Prognostic Implication of COL6A6 in Lung Adenocarcinoma

Qiu

Guo

et al. 2021

Front. Oncol.

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“…Based on the fact that immune cells have significant value in evaluating the prognosis of various cancers, especially HCC ( Zhuang et al, 2020 ), this study constructs the prognosis model of HCC through identifying immune-related genes co-expressed with immune cells which were associated with the prognosis of HCC. In this study, Immune Cell Abundance Identifier (ImmuCellAI), weighted gene co-expression network analysis (WGCNA), LASSO Cox analysis, receiver operating characteristic (ROC) curve analysis, univariate Cox analysis, and multivariate Cox analysis were applied to identify immune-related genes in the HCC microenvironment and construct a risk-scoring model, which exhibited benign prognostic value in TCGA cohort and GEO cohort.…”

Section: Introductionmentioning

confidence: 99%

Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

Huang

Jiang

Huang

et al. 2021

Front. Mol. Biosci.

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The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

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“…Previous studies have classified the immune subtypes of thyroid cancer (Xie et al, 2020;Zhi et al, 2020) and grouped risk scores (Zhuang et al, 2020), but no comprehensive analysis or comparison of the two approaches have been conducted. This study is the first to focus on these approaches and use them to construct two signatures: ICIscore and Riskscore.…”

Section: Introductionmentioning

confidence: 99%

The landscape of tumors-infiltrate immune cells in papillary thyroid carcinoma and its prognostic value

Huang

Liu

et al. 2021

PeerJ

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Introduction Thyroid cancer is a very common malignant tumor in the endocrine system, while the incidence of papillary thyroid carcinoma (PTC) throughout the world also shows a trend of increase year by year. In this study, we constructed two models: ICIscore and Riskscore. Combined with these two models, we can make more accurate and reasonable inferences about the prognosis of PTC patients. Methods We selected 481 PTC samples from TCGA and 147 PTC samples from GEO (49 samples in GSE33630, 65 samples in GSE35570 and 33 samples in GSE60542). We performed consistent clustering for them and divided them into three subgroups and screened differentially expressed genes from these three subgroups. Then we divided the differential genes into three subtypes. We also distinguished the up-regulated and down-regulated genes and calculated ICIscore for each PTC sample. ICIscore consists of two parts: (1) the PCAu was calculated from up-regulated genes. (2) the PCAd was calculated from down-regulated genes. The PCAu and PCAd of each sample were the first principal component of the relevant gene. What’s more, we divided the patients into two groups and constructed mRNA prognostic signatures. Additionally we also verified the independent prognostic value of the signature. Results Though ICIscore, we were able to observe the relationship between immune infiltration and prognosis. The result suggests that the activation of the immune system may have both positive and negative consequences. Though Riskscore, we could make more accurate predictions about the prognosis of patients with PTC. Meanwhile, we also generated and validated the ICIscore group and Riskscore group respectively. Conclusion All the research results show that by combining the two models constructed, ICIscore and Riskscore, we can make a more accurate and reasonable inference about the prognosis of patients with clinical PTC patients. This suggests that we can provide more effective and reasonable treatment plan for clinical PTC patients.

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Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis

Cited by 16 publications

References 39 publications

Comprehensive Analysis of the Immune and Prognostic Implication of COL6A6 in Lung Adenocarcinoma

Comprehensive Analysis of the Immune and Prognostic Implication of COL6A6 in Lung Adenocarcinoma

Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

The landscape of tumors-infiltrate immune cells in papillary thyroid carcinoma and its prognostic value

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