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Using Tumor-Infiltrating Immune Cells and a ceRNA Network Model to Construct a Prognostic Analysis Model of Thyroid Carcinoma.

ABSTRACT: Thyroid carcinoma is a solid malignant tumor that has had a fast-growing incidence in recent years. Our research used thyroid carcinoma gene expression profiling from TCGA (The Cancer Genome Atlas) database to identify differentially expressed ceRNAs. Using the gene expression profiling from 502 carcinoma thyroid tissues and 58 normal thyroid tissues from the TCGA database, we established the thyroid carcinoma-specific competitive endogenous RNA (ceRNA) network and found nine overall survival (OS)-associated genes (PRDM1, TGFBR3, E2F1, FGF1, ADAM12, ALPL, RET, AL928654.2, AC128688.2). We quantified the proportions of immune cells using the algorithm "CIBERSORT", found three OS-associated immune cells (memory B cells, M0 macrophages, and activated dendritic cells), and established a thyroid carcinoma-specific immune cell network based on that. The good reliabilities AUC (area under the curve) of 10-year survival (0.955, 0.944, respectively) were accessed from the nomograms of genes and immune cells. Subsequently, by conducting co-expression analyses, we found a potential regulation network among ceRNAs and immune cells. Besides, we found that ALPL (alkaline phosphatase) and hsa-miR-204-5p were significantly correlated and that ALPL was related to activated dendritic cells. We took advantage of multi-dimensional databases to verify our discovery. Besides, immunohistochemistry (IHC) assays were conducted to detect the expression of a dendritic cell marker (CD11c) and ALPL in thyroid carcinoma (TC) and paracancerous tissues. In summary, our study found a potential mechanism in which hsa-miR-204-5p regulated ALPL in activated dendritic cells, which may allow them to play a critical role in thyroid carcinoma. These findings provide potential prognostic biomarkers and therapeutic targets for thyroid carcinoma.

SUBMITTER: Zhang F

PROVIDER: S-EPMC8204697 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Project description:Introduction: Gastric cancer (GC) is the fifth frequent malignancy and is responsible for the third leading cause of cancer-related deaths. Gastric cancer is an aging-related disease, with incidence and mortality rates increasing with aging. The development of GC is affected by lncRNAs, miRNAs, and mRNAs at the transcriptional and posttranscriptional levels. This study aimed to establish a prognostic panel for GC based on competing endogenous RNA (ceRNA) networks. Methods: RNA sequences were obtained from the TCGA database. Different expressions of RNAs were scrutinized with the EdgeR package. The ceRNA network was built using the starBase database and the Cytoscape. The prognostic panel was constituted with the LASSO algorithm. We developed a nomogram comprising clinical characteristic and risk score. The receiver operating characteristic (ROC) was used to evaluate the accuracy of the nomogram prediction. Hub RNAs expressions were detected by qPCR, immunohistochemistry and western blot respectively. Clinical relevance and survival analyses were analyzed. The relationship between RNAs and immune infiltrations, as well as immune checkpoints, was analyzed and evaluated using the CIBERSORT, TIMER and TISIDB databases. Results: Four DElncRNAs, 21 DEmiRNAs and 45 DEmRNAs were included in the ceRNA network. A 3-element panel (comprising lncRNA PVT1, hsa-miR-130a-3p and RECK) with poor overall survival (OS) was established and qPCR was applied to validate the expressions of hub RNAs. Hub RNAs were firmly associated with T, M, and N stage. The CIBERSORT database showed that the high lassoScore group exhibited a significantly high ratio of resting memory CD4+ T cells, M2 macrophages and a significantly low ratio of activated memory CD4+ T cells and M1 macrophages. According to the TIMER database, this panel was linked to immune infiltrations and immune cell gene markers. TISIDB database indicated that RECK was positively correlated with immune checkpoints (including CD160, CD244, PDCD1, and TGFBR1). Discussion: A novel triple prognostic panel of GC constructed based on the ceRNA network was associated with clinical prognostic, clinicopathological features, immune infiltrations, immune checkpoints and immune gene markers. This panel might provide potential therapeutic targets for GC and more experimental verification research is needed.

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Using Tumor-Infiltrating Immune Cells and a ceRNA Network Model to Construct a Prognostic Analysis Model of Thyroid Carcinoma.

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