Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

Identification and validation of a novel prognostic signature and key genes related to development of anaplastic thyroid carcinoma

BACKGROUND

Anaplastic thyroid carcinoma (ATC) is a rare but the most aggressive type of thyroid carcinoma. Nevertheless, limited advances were made to reduce mortality and improve survival over the last decades. Therefore, identifying novel diagnostic biomarkers and therapeutic targets for ATC patients is still needed.

MATERIALS AND METHODS

RNA sequencing data and corresponding clinical features were available from GEO and TCGA databases. We integrated WGCNA and PPI network analysis to identify hub genes associated with ATC development, and RT-qPCR was employed for data verification. Univariate and LASSO Cox regression analyses were used to generate prognostic signatures.

RESULTS

Based on PPI and WGCNA, 6 hub genes were identified, namely KIF2C, PBK, TOP2A, CDK1, KIF20A, and ASPM, which play vital roles in ATC development. Subsequently, RT-qPCR experiments showed that most of these genes were significantly upregulated in CAL-62 cells compared to Nthy-ori 3-1 cells. Moreover, a prognostic signature featuring GPSM2, FGF5, ASXL3, CYP4B1, CLMP, and DUXAP9 was generated, which was also verified by RT-qPCR results and proved as an independent predictor of poorer prognosis of ATC. Additionally, a nomogram incorporating the risk score and clinicopathological parameters was further constructed for accurate prediction of 1-, 3- and 5-year survival probabilities of ATC.

CONCLUSIONS

Our study identified 6 key genes critical to ATC development and constructed a prognostic signature. These findings provide reliable biomarkers and a relatively comprehensive tumorigenesis profile of ATC, which may inform future strategies for clinical diagnosis and pharmaceutical design.

Harnessing Immunity to Treat Advanced Thyroid Cancer

The incidence of thyroid cancer (TC) has increased over the past 30 years. Although differentiated thyroid cancer (DTC) has a good prognosis in most patients undergoing total thyroidectomy followed by radioiodine therapy (RAI), 5-10% of patients develop metastasis. Anaplastic thyroid cancer (ATC) has a low survival rate and few effective treatments have been available to date. Recently, tyrosine kinase inhibitors (TKIs) have been successfully applied to RAI-resistant or non-responsive TC to suppress the disease. However, TC eventually develops resistance to TKIs. Immunotherapy is a promising treatment for TC, the majority of which is considered an immune-hot malignancy. Immune suppression by TC cells and immune-suppressing cells, including tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, is complex and dynamic. Negative immune checkpoints, cytokines, vascular endothelial growth factors (VEGF), and indoleamine 2,3-dioxygenase 1 (IDO1) suppress antitumor T cells. Basic and translational advances in immune checkpoint inhibitors (ICIs), molecule-targeted therapy, tumor-specific immunotherapy, and their combinations have enabled us to overcome immune suppression and activate antitumor immune cells. This review summarizes current findings regarding the immune microenvironment, immunosuppression, immunological targets, and immunotherapy for TC and highlights the potential efficacy of immunotherapy.

Retracted: Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

[This retracts the article DOI: 10.1155/2022/2244228.].

Update on current diagnosis and management of anaplastic thyroid carcinoma

Well-differentiated thyroid carcinoma has a favorable prognosis with a 5-year survival rate of over 95%. However, the undifferentiated or anaplastic type accounting for < 0.2%, usually in elderly individuals, exhibits a dismal prognosis with rapid growth and disappointing outcomes. It is the most aggressive form of thyroid carcinoma, with a median survival of 5 mo and poor quality of life (airway obstruction, dysphagia, hoarseness, persistent pain). Early diagnosis and staging are crucial. Diagnostic tools include biopsy (fine needle aspiration, core needle, open surgery), high-resolution ultrasound, computed tomography, magnetic resonance imaging, [(18)F]fluoro-D-glucose positron emission tomo-graphy/computed tomography, liquid biopsy and microRNAs. The BRAF gene (BRAF-V600E and BRAF wild type) is the most often found molecular factor. Others include the genes RET, KRAS, HRAS, and NRAS. Recent management policy is based on surgery, even debulking, chemotherapy (cisplatin or doxorubicin), radiotherapy (adjuvant or definitive), targeted biological agents and immunotherapy. The last two options constitute novel hopeful management modalities improving the overall survival in these otherwise condemned patients. Anti-programmed death-ligand 1 antibody immunotherapy, stem cell targeted therapies, nanotechnology achievements and artificial intelligence imple-mentation provide novel promising alternatives. Genetic mutations determine molecular pathways, thus indicating novel treatment strategies such as anti-BRAF, anti-vascular endothelial growth factor-A, and anti-epidermal growth factor receptor. Treatment with the combination of the BRAF inhibitor dabrafenib and the MEK inhibitor trametinib has been approved by the Food and Drug Administration in cases with BRAF-V600E gene mutations and is currently the standard care. This neoadjuvant treatment followed by surgery ensures a two-year overall survival of 80%. Prognostic factors for improved outcomes have been found to be younger age, earlier tumor stage and radiation therapy. A multidisciplinary approach is necessary, and the therapeutic plan should be individualized based on surveillance and epidemiology end results.

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis

Background

Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear.

Method

Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database.

Results

A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs.

Conclusion

Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI.