The exploration of new therapeutic targets for HPV‐negative head and neck squamous cell cancer through the construction of a ceRNA network and immune microenvironment analysis

Wang, Zuoyuan; Liu, Tianyi; Li, Guangqi; Cao, Zhipeng

doi:10.1002/jcb.29615

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…The "limma" R package was used to identify differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between high-and low-stromal or immune score groups with |log2FC| > 1 and adjusted P value < 0.05 as the cutoff criteria, and then intersection was taken (14,15). Meanwhile, differentially expressed miRNAs (DEmiRNAs) between AML and normal samples were identified after normalization, |log2FC|>1 and adjusted P value <0.05 was defined as the cutoff criteria (16,17). The "pheatmap" and "ggplot2" R packages were involved in drawing heatmap and volcanoes of the differential genes.…”

Section: Aml Transcriptome Data and Clinical Informationmentioning

confidence: 99%

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Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Cheng

Wang

et al. 2021

Front. Oncol.

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Acute myeloid leukemia (AML) is malignant hematologic tumors with frequent recurrence and cause high mortality. Its fate is determined by abnormal intracellular competitive endogenous RNA (ceRNA) network and extracellular tumor microenvironment (TME). This study aims to build a ceRNA network related to AML TME to explore new prognostic and therapeutic targets. The RNA expression data of AML were obtained from The Cancer Genome Atlas (TCGA) database. First, we used the ESTIMATE algorithm to calculate the immune cells and stromal cells infiltration scores in the TME and found that all scores were highly correlated with AML’s prognostic characteristics. Subsequently, differentially expressed mRNAs and lncRNAs between high and low score groups were identified to construct a TME-related ceRNA network. Further, the Cox-lasso survival model was employed to screen out the hub prognostic ceRNA network composed of two mRNAs (EPB41L3, COL2A1), three miRNAs (hsa-mir-26a-5p, hsa-mir-148b-3p, hsa-mir-148a-3p), and two lncRNAs (CYP1B1-AS1, C9orf106), and construct nomograms. Finally, we used CIBERSORT algorithm and Kaplan-Meier survival analysis to identify the prognostic TME immune cells and found that naive B cells, M2-type macrophages, and helper follicular T cells were related to prognosis, and the hub ceRNAs were highly correlated with immune cell infiltration. This study provided a new perspective to elucidate how TME regulates AML process and put forward the new therapy strategies combining targeting tumor cells with disintegrating TME.

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Section: Aml Transcriptome Data and Clinical Informationmentioning

confidence: 99%

“…Analyses were performed with 100 permutations with the default statistical parameters to improve the credibility of the results. Samples with a CIBERSORT output of P < 0.05 were considered to be eligible for further analysis (16,23).…”

Section: Tme Immune Cell Infiltration Analysismentioning

confidence: 99%

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Cheng

Wang

et al. 2021

Front. Oncol.

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“…[7] lncRNAs competitively bind to miRNAs, to regulate the expression level of mRNAs and involve in the regulation of biological behaviors of tumor cells. [8–10] The ceRNA network plays a vital role in the development of various malignant tumors. [11–13] A large number of studies have shown that miRNA can guide RNA-induced silencing complex (RISC) to bind to target mRNA, leading to RNA degradation or translational inhibition.…”

Section: Introductionmentioning

confidence: 99%

The analysis of tumor-infiltrating immune cell and ceRNA networks in laryngeal squamous cell carcinoma

Dong

et al. 2022

Medicine

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Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common forms of head and neck cancers. However, few studies have focused on the correlation between competing endogenous RNA (ceRNAs) and immune cells in LSCC.Methods: RNAseq expression of LSCC and adjacent tissues were downloaded from The Cancer Genome Atlas to establish a ceRNA network. The key gene in ceRNA was screened by the cox regression analysis to establish a prognostic risk assessment model. The CIBERSORT algorithm was then used to screen important tumor-infiltrating cells related to LSCC. Finally, co-expression analysis was applied to explore the relationship between key genes in the ceRNA network and tumor-infiltrating cells. The external datasets were used to validate critical biomarkers.Results: We constructed a prognostic risk assessment model of key genes in the ceRNA network. As it turned out, Kaplan-Meier survival analysis showed significant differences in overall survival rates between high-risk and low-risk groups (P < .001). The survival rate of the high-risk group was drastically lower than that of the low-risk group, and the AUC of 1 year, 3 years, and 5 years were all above 0.7. In addition, some immune infiltrating cells were also found to be related to LSCC. In the co-expression analysis, there is a negative correlation between plasma cells and TUBB3 (r = −0.33, P = .0013). External dataset validation also supports this result. Conclusion:In this study, we found that some key genes (SLC35C1, CLDN23, HOXB7, STC2, TMEM158, TNFRSF4, TUBB3) and immune cells (plasma cells) may correspond to the prognosis of LSCC.Abbreviations: ceRNA = competing endogenous RNA, DElncRNAs = differentially expressed lncRNAs, DEmRNAs = differentially expressed mRNAs, GEO = Gene Expression Omnibus, GO = Gene Ontology, HNSCC = head and neck squamous cell carcinoma, KEGG = Kyoto Encyclopedia of Genes and Genomes, LSCC = laryngeal squamous cell carcinoma, OS = overall survival, TCGA = The Cancer Genome Atlas.

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“…Zhou et al constructed a ceRNA-related signature and speculated that the interactions among KCNQ1OT1, hsa-miR-148a-3p, ITGA5, and naive B cells might closely correlate with the initiation and progression of HNSCC [ 15 ]. Wang et al investigated the role of the immune microenvironment in the development and prognosis of HPV-negative HNSCC tumors by constructing a ceRNA network [ 16 ]. Yang et al identified five lncRNAs (MIR4435-2HG, CASC9, LINC01980, STARD4-AS1, and MIR99AHG) with remarkable association with OS of HNSCC patients and one lncRNA (PART1) with a superior performance in differentiating HNSCC tissues from non-HNSCC normal tissues [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Immune Infiltration-Related ceRNA Network Revealing Potential Biomarkers for Prognosis of Head and Neck Squamous Cell Carcinoma

et al. 2022

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Background. Head and neck squamous cell carcinoma (HNSCC) is a frequently lethal malignancy, and the mortality is considerably high. The tumor microenvironment (TME) has been identified as a critical participation in cancer development, treatment, and prognosis. However, competing endogenous RNA (ceRNA) networks grouping with immune/stromal scores of HNSCC patients need to be further illustrated. Therefore, our study aimed to provide clues for searching promising prognostic markers of TME in HNSCC. Materials and Methods. ESTIMATE algorithm was used to calculate immune scores and stromal scores of the enrolled HNSCC patients. Differentially expressed genes (DEGs), lncRNAs (DELs), and miRNAs (DEMs) were identified by comparing the expression difference between high and low immune/stromal scores. Then, a ceRNA network and protein-protein interaction (PPI) network were constructed for selecting hub regulators. In addition, survival analysis was performed to access the association between immune scores, stromal scores, and differentially expressed RNAs in the ceRNA network and the overall survival (OS) of HNSCC patients. Then, the GSE65858 datasets from Gene Expression Omnibus (GEO) database was used for verification. At last, the difference between the clinical characteristics and immune cell infiltration in different expression groups of IL10RA, PRF1, and IL2RA was analyzed. Results. Survival analysis showed a better OS in the high immune score group, and then we constructed a ceRNA network composed of 97 DEGs, 79 DELs and 22 DEMs. Within the ceRNA network, FOXP3, IL10RA, STAT5A, PRF1, IL2RA, miR-148a-3p, miR-3065-3p, and lncRNAs, including CXCR2P1, HNRNPA1P21, CTA-384D8.36, and IGHV1OR15-2, were closely correlated with the OS of HNSCC patients. Especially, using the data from GSE65858, we successfully verified that IL10RA, PRF1, and IL2RA were not only significantly upregulated in patients high immune scores, but also their high expressions were associated with longer survival time. In addition, stratified analysis showed that PRF1 and IL2RA might be involved in the mechanism of tumor progress. Conclusion. In conclusion, we constructed a ceRNA network related to the TME of HNSCC, which provides candidates for therapeutic intervention and prognosis evaluation.

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The exploration of new therapeutic targets for HPV‐negative head and neck squamous cell cancer through the construction of a ceRNA network and immune microenvironment analysis

Cited by 9 publications

References 37 publications

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

The analysis of tumor-infiltrating immune cell and ceRNA networks in laryngeal squamous cell carcinoma

Immune Infiltration-Related ceRNA Network Revealing Potential Biomarkers for Prognosis of Head and Neck Squamous Cell Carcinoma

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