Comprehensive Analysis of the Functions and Prognostic Value of RNA-Binding Proteins in Thyroid Cancer

Bioinformatic identification reveals a m6A-binding protein, IGF2BP2, as a novel tumor-promoting gene signature in thyroid carcinoma

N6-methyladenosine (m6A) modification plays a crucial role in thyroid carcinoma (THCA). Insulin-like growth factor 2 binding protein 2 (IGF2BP2) is a m6A-binding protein. We aimed to explore the effect of IGF2BP2 on the development of THCA. Differentially expressed genes (DEGs) were screened from GSE50901 and GSE60542 datasets. LinkedOmics, Genebank, and Sequence-based RNA Adenosine Methylation Site Predictor databases were employed to find potential m6A modification sites. Protein-protein interaction network and receiver-operating characteristic curves were applied to determine hub genes of THCA. ESTIMATE revealed the effect of IGF2BP2 on tumor immunity. The mRNA expression of IGF2BP2 was detected using real-time quantitative polymerase chain reaction. The viability, migration, and invasion were assessed by Cell Counting Kit-8, wound healing, and transwell assays. A total of 166 common DEGs were identified from GSE50901 and GSE60542 datasets. One m6A-related gene, IGF2BP2, was differentially expressed in THCA and selected as the research target. The hub genes (CD44, DCN, CXCL12, ICAM1, SDC4, KIT, CTGF, and FMOD) were identified with high prediction values for THCA. Subsequently, the target genes of IGF2BP2, SDC4, and ICAM1, which had potential m6A modification sites, were screened out based on the hub genes. IGF2BP2 was upregulated in THCA and IGF2BP2 expression was positively correlated with immune infiltration in THCA. Additionally, knockdown of IGF2BP2 inhibited the proliferation, invasion, and migration of THCA cells. IGF2BP2 has a contributory effect on the progression of THCA, which is a novel biomarker and a therapeutic target for THCA.

Advances in the understanding of nuclear pore complexes in human diseases

BACKGROUND

Nuclear pore complexes (NPCs) are sophisticated and dynamic protein structures that straddle the nuclear envelope and act as gatekeepers for transporting molecules between the nucleus and the cytoplasm. NPCs comprise up to 30 different proteins known as nucleoporins (NUPs). However, a growing body of research has suggested that NPCs play important roles in gene regulation, viral infections, cancer, mitosis, genetic diseases, kidney diseases, immune system diseases, and degenerative neurological and muscular pathologies.

PURPOSE

In this review, we introduce the structure and function of NPCs. Then We described the physiological and pathological effects of each component of NPCs which provide a direction for future clinical applications.

METHODS

The literatures from PubMed have been reviewed for this article.

CONCLUSION

This review summarizes current studies on the implications of NPCs in human physiology and pathology, highlighting the mechanistic underpinnings of NPC-associated diseases.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

MEX3A induces the development of thyroid cancer via targeting CREB1

Thyroid cancer is a prevalent form of endocrine cancer, and its global incidence has been steadily increasing. MEX3A is a protein that is known to be highly expressed in various human malignant tumors, including thyroid cancer, and it has been linked to patient prognosis. However, the molecular mechanisms underlying MEX3A's tumorigenic capabilities in thyroid cancer are not fully understood. In this study, we aimed to investigate the role of MEX3A in thyroid cancer. We confirmed that MEX3A was overexpressed in both thyroid cancer tissues and cell lines. Additionally, we found a positive correlation between high levels of MEX3A and the AJCC stage. To further understand the functional significance of MEX3A in thyroid cancer, we depleted MEX3A expression in B-CPAP and TPC-1 cells. Interestingly, we observed a significant reduction in thyroid cancer cell proliferation and migration, as well as ameliorated cell apoptosis and arrested tumor growth upon MEX3A depletion. These findings strongly suggested that MEX3A played a critical role in the development of thyroid cancer. Furthermore, our study uncovered an important interaction between MEX3A and CREB1 (cAMP response element-binding protein 1). The interaction between MEX3A and CREB1 appeared to contribute to the tumor-promoting effects of MEX3A in thyroid cancer by directly targeting CREB1. Silencing CREB1 was observed to alleviate the malignant phenotypes promoted by MEX3A in thyroid cancer cells. Together, this study highlighted the importance of the MEX3A-CREB1 interaction in thyroid cancer development and suggested the therapeutic potential of targeting MEX3A for the treatment of this disease.

An RNA-binding protein-related risk signature can predict the prognosis and tumor immunity of patients with testicular germ cell tumors

BACKGROUND

The functions of RNA-binding proteins (RBPs) in the occurrence and development of tumors remain largely unexplored. We established a risk signature based on RBPs to predict the prognosis, tumor-related immunity, and treatment benefits of patients with testicular germ cell tumors (TGCTs).

METHODS

A risk signature was built based on RBPs closely related to survival obtained from TGCT data in The Cancer Genome Atlas (TCGA) database. The ability of the signature to predict prognosis was analyzed by survival curves and Cox regression. The risk signature was validated using the Gene Expression Omnibus (GEO) database. The connection between tumor immunity and the risk score was evaluated. Risk score-related drug sensitivity and biofunctions were also explored.

RESULTS

A risk signature including four selected RBP genes (PARP12, USB1, POLR2E and EED) was established. The prognosis of high-risk TGCT patients was worse than that of low-risk TGCT patients. The risk score was considered a critical factor closely related to prognosis, as determined via Cox regression, and was also closely associated with multiple characteristics of tumor immunity, chemotherapy drugs and biofunctions.

CONCLUSION

The established risk signature including four selected RBPs in TGCTs could predict the prognosis, tumor-related immunity and treatment benefits of patients with TGCTs. Utilization of this signature could help clinicians make personalized treatment decisions.

Construction of a Signature Model to Predict the Radioactive Iodine Response of Papillary Thyroid Cancer

Papillary thyroid cancer (PTC) accounts for about 90% of thyroid cancer. There are approximately 20%-30% of PTC patients showing disease persistence/recurrence and resistance to radioactive iodine (RAI) treatment. For these PTC patients with RAI refractoriness, the prognosis is poor. In this study, we aimed to establish a comprehensive prognostic model covering multiple signatures to increase the predictive accuracy for progression-free survival (PFS) of PTC patients with RAI treatment. The expression profiles of mRNAs and miRNAs as well as the clinical information of PTC patients were extracted from TCGA and GEO databases. A series of bioinformatics methods were successfully applied to filtrate a two-RNA model (IPCEF1 and hsa-mir-486-5p) associated with the prognosis of RAI-therapy. Finally, the RNA-based risk score was calculated based on the Cox coefficient of the individual RNA, which achieved good performances by the time-dependent receiver operating characteristic (tROC) curve and PFS analyses. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and tumor stage), was assessed by tROC curves. Collectively, our study demonstrated high precision in predicting the RAI response of PTC patients.

Aberrant Expression of BUB1B Contributes to the Progression of Thyroid Carcinoma and Predicts Poor Outcomes for Patients

Objective: This study aimed to clarify the function and potential mechanism of BUB1B in THCA.

Methods: Expression of BUB1B in THCA was firstly determined, and its important prognostic value was then demonstrated. The potential mechanism was initially predicted by KEGG analysis. To explore the specific function of BUB1B in THCA, we used lentivirus infection to knock down the BUB1B, and then performed flow cytometry, colony formation, transwell, and wound-healing assays. Related protein expression was detected through western blotting. Additionally, we predicted the BUB1B-regulated pathways involved in THCA by GSEA analysis.

Results: BUB1B expression was highly increased in THCA tissues relative to normal controls. We further found that BUB1B was essential for tumor cell proliferation, and BUB1B high expression predicted a shorter PFS time of THCA patients. More importantly, Cox regression determined the BUB1B as an independent prognostic factor for PFS in THCA. BUB1B was initially found to participate in the cell cycle pathway from KEGG analysis. Unexpectedly, we did not detect the disturbing effect on the cell cycle distribution of THCA cells with BUB1B knockdown. But, BUB1B knockdown inhibited the proliferation, invasion, and migration of THCA cells, as well as increased apoptotic cells, and the results were further confirmed by western blotting. Through GSEA analysis, we predicted a positive correlation between BUB1B and metastasis-related pathways such as mTOR and NF-kappa B signaling pathways.

Conclusions: Present study identified BUB1B as a promising clinical prognostic factor in THCA, as well as a potential novel therapeutic target for cancer treatment.

Integrated analyses of an RNA binding protein-based signature related to tumor immune microenvironment and candidate drugs in osteosarcoma

OBJECTIVE

Osteosarcoma is the most frequent primary bone malignancy, associated with frequent recurrence and lung metastasis. RNA-binding proteins (RBPs) are pivotal in regulating several aspects of cancer biology. Nonetheless, interaction between RBPs and the osteosarcoma immune microenvironment is poorly understood. We investigated whether RBPs can predict prognosis and immunotherapy response in osteosarcoma patients.

METHODS

We constructed an RBP-related prognostic signature (RRPS) by univariate coupled with multivariate analyses and verified the independent prognostic efficacy of the signature. Single-sample Gene Set Enrichment Analysis (ssGSEA) along with ESTIMATE analysis were carried out to investigate the variations in immune characteristics between subgroups with various RRPS-scores. Furthermore, we investigatedpossible small molecule drugs using the connectivity map database and validated the expression of hub RBPs by qRT-PCR.

RESULTS

The RRPS, consisting of seven hub RBPs, was an independent prognostic factor compared to traditional clinical features. The RRPS could distinguish immune functions, immune score, stromal score, tumor purity and tumor infiltration by immune cells in different osteosarcoma subjects. Additionally, patients with high RRPS-scores had lower expression of immune checkpoint genes than patients with low RRPS-scores. We finally identified six small molecule drugs that may improve prognosis in osteosarcoma patients and substantiated notable differences in the contents of these RBPs.

CONCLUSION

We evaluated the prognostic value and clinical application of an RBPs-based prognostic signature and identified promising biomarkers to predict immune cell infiltration and immunotherapy response in osteosarcoma.