Transcriptome analysis reveals the link between lncRNA-mRNA co-expression network and tumor immune microenvironment and overall survival in head and neck squamous cell carcinoma

Zhong, Zhaoming; Hong, Min Hee; Chen, Xiao; Xi, Yan; Xu, Yuanyuan; Kong, Deyu; Deng, Jun; Li, Yun; Hu, Rui; Sun, Chuanzheng; Liang, Jin

doi:10.1186/s12920-020-0707-0

Cited by 25 publications

(23 citation statements)

References 50 publications

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“…Whether it is tumor immune cells or tumor immune-related genes, there is often a network-based synergistic relationship between them often exists ( 37 – 39 ). If the immune infiltration state of LUAD is classified and confirmed, it is important to evaluate which are the coordinate expression genes, and the correlation between them and the immune infiltration in LUAD.…”

Section: Discussionmentioning

confidence: 99%

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

et al. 2021

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BackgroundLung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects.MethodsTCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis, and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the four genes and tumor stages was constructed and confirmed in two GEO data sets.ResultsAmong the three subtypes—high, medium, and low immune infiltration subtype—the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The four key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2, and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these four genes through the S1PR1. The risk score signature based on these four genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets.ConclusionsThe prognosis of LUAD patients could be predicted by the constructed risk score signature based on the four genes, making this score a potential independent biomarker. The screening, identification, and analysis of these four genes could contribute to the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.

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Section: Discussionmentioning

confidence: 99%

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

et al. 2021

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“…Compared to a certain number of studies carried out to explore IRL signatures in several human malignancies [48][49][50][51][52][53][54], this was the rst study based on a comprehensive analysis that focused on IRL signature in CC. However, our study has some limitations.…”

Section: Discussionmentioning

confidence: 99%

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

Zheng

Tong

Cao

et al. 2020

Preprint

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Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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“…12,13 Furthermore, ln-cRNAs play a vital role in the tumor microenvironment (TME) and are correlated with patient prognosis. [14][15][16][17] However, the effects of immune-related lncRNAs in the TME and predicting the prognosis of HNSCC remain unknown.…”

Section: Introductionmentioning

confidence: 99%

“…Long noncoding RNAs (lncRNAs) are involved in the process of tumor development including tumorigenesis and metastasis 12,13 . Furthermore, lncRNAs play a vital role in the tumor microenvironment (TME) and are correlated with patient prognosis 14‐17 . However, the effects of immune‐related lncRNAs in the TME and predicting the prognosis of HNSCC remain unknown.…”

Section: Introductionmentioning

confidence: 99%

An immune‐related seven‐lncRNA signature for head and neck squamous cell carcinoma

Chen

Luo

et al. 2021

Cancer Medicine

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Transcriptome analysis reveals the link between lncRNA-mRNA co-expression network and tumor immune microenvironment and overall survival in head and neck squamous cell carcinoma

Cited by 25 publications

References 50 publications

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

An immune‐related seven‐lncRNA signature for head and neck squamous cell carcinoma

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