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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:BACKGROUND:The tumor immune microenvironment has become the focus of research in clear cell renal cell carcinoma (ccRCC) due to its important role in immune surveillance post nephrectomy. This study investigates the correlation of tumor infiltrating immune cell characteristics with rates of recurrence following surgery in localized ccRCC. METHODS:We morphologically identified and scored tumor infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) stained slides of patients with localized ccRCC (stage ?T1b excluding stage IV). The University of Alabama at Birmingham (UAB) dataset (n =?159) was used to discover and the Fox Chase Cancer Center (FCCC) dataset (n =?198) was used to validate the results of morphologic immune cell analysis. We then performed gene expression analysis using the Immune Profile panel by NanoString in the UAB cohort and identified immune cells and pathways associated with recurrence, followed by validation in the Cancer Genome Atlas (TCGA) ccRCC dataset. Infiltrating immune cell types were identified by gene expression deconvolution. RESULTS:The presence of TILs identified by morphology correlated with higher T cell, Th1, CD8+ T and Treg gene signatures. Recurrence was associated with lower T cells and higher neutrophils. Higher Teffector (Teff)/Treg ratio correlated with lower rate of recurrence and was validated in the TCGA dataset. Genes associated with adaptive immune response were downregulated in tumors that recurred. Unsupervised hierarchical clustering identified a subset of patients with over-expression of adaptive response genes including CD8, CD3, GZMA/B, PRF1, IDO1, CTLA4, PDL1, ICOS and TIGIT. These patients had higher morphologic lymphocyte infiltration and T cell gene expression. Higher levels of TILs identified by morphology correlated with higher rates of recurrence in our discovery dataset but not in our validation set. CONCLUSIONS:Recurrence of ccRCC following surgery was associated with lower T cell infiltrate, lower adaptive immune response and higher neutrophil gene expression. Presence of higher Teff/Treg ratio correlated with lower recurrence.

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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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