A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes

Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients

Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.

Integration of scRNA and bulk RNA-sequence to construct the 5-gene molecular prognostic model based on the heterogeneity of thyroid carcinoma endothelial cell

Thyroid cancer (TC) is a kind of cancer with high heterogeneity, which leads to significant difference in prognosis. The prognostic molecular processes are not well understood. Cancer cells and tumor microenvironment (TME) cells jointly determine the heterogeneity. However, quite a little attention was paid to cells in the TME in the past years. In this study, we not only reveal that endothelial cells (ECs) are strongly associated with the progress of papillary thyroid cancer (PTC) using single-cell RNA-seq (scRNA-seq) data downloaded from Gene Expression Omnibus (GEO) and WGCNA, but also screen 5 crucial genes of ECs: CLDN5, ABCG2, NOTCH4, PLAT, and TMEM47. Furthermore, the 5-gene molecular prognostic model is constructed, which can predict how well a patient will do on PD-L1 blockade immunotherapy for TC and evaluate prognosis. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrates that PLAT is decreased in TC and the increase of PLAT can restrain the migratory capacity of TC cells. Meanwhile, in TC cells, PLAT suppresses VEGFa/VEGFR2-mediated human umbilical vascular endothelial cell (HUVEC) proliferation and tube formation. Totally, we construct the 5-gene molecular prognostic model from the perspective of EC and provide a new idea for immunotherapy of TC.

Integrative analysis of circular RNA regulatory network in papillary thyroid carcinoma

Papillary thyroid cancer (PTC) is the most common type of endocrine cancer worldwide. Generally, PTC has an excellent prognosis; however, lymph node metastases and recurrences occur frequently. Over the last decade, circular RNAs (circRNAs), a large class of noncoding RNAs (ncRNAs), have emerged as key regulators of various tumor progression pathways. Here, we aimed to identify novel circRNAs as PTC biomarkers. Differentially expressed circRNAs and mRNAs were analyzed using public datasets from the Gene Expression Omnibus and Cancer Genome Atlas. In addition, we screened for target miRNAs using online prediction databases. Based on these results, we established a circRNA-miRNA-mRNA regulatory network associated with PTC, in which protein-protein interaction networks led to the identification of hub genes. Functional enrichment and survival analyses were performed to gain insights into the biological mechanisms of circRNA involvement. As a result, we found that two circRNAs (hsa_circ_0041829 and has_circ_0092299), four miRNAs (miR-369, miR-486, miR-574, and miR-665), and nine hub genes (BBC3, E2F1, FYN, MAG, SDC1, SDC3, SNAP25, TK1, and TYMS) play significant roles in PTC progression. This study provides a novel framework for understanding the roles of circRNA-miRNA-mediated gene regulation in PTC. It also introduces potential therapeutic targets and prognostic biomarkers, which may serve as a basis for developing targeted therapeutic interventions for PTC.