Identification of Transcriptional Pattern Related to Immune Cell Infiltration With Gene Co-Expression Network in Papillary Thyroid Cancer

Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning

There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.

Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.

Prognosis and therapy in thyroid cancer by gene signatures related to natural killer cells

BACKGROUND

Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA).

METHODS

The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the "Seurat" R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package "ConsensusClusterPlus". The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the "glmnet" R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The "maftools" R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups.

RESULTS

One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis.

CONCLUSION

Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.

New Insights into Immune Cells and Immunotherapy for Thyroid Cancer

Thyroid cancer (TC) is the most common endocrine malignancy worldwide, and the incidence of TC has gradually increased in recent decades. Differentiated thyroid cancer (DTC) is the most common subtype and has a good prognosis. However, advanced DTC patients with recurrence, metastasis and iodine refractoriness, as well as more aggressive subtypes such as poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC), still pose a great challenge for clinical management. Therefore, it is necessary to continue to explore the inherent molecular heterogeneity of different TC subtypes and the global landscape of the tumor immune microenvironment (TIME) to find new potential therapeutic targets. Immunotherapy is a promising therapeutic strategy that can be used alone or in combination with drugs targeting tumor-driven genes. This article focuses on the genomic characteristics, tumor-associated immune cell infiltration and immune checkpoint expression of different subtypes of TC patients to provide guidance for immunotherapy.