Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis

Identification of hub genes and key modules in laryngeal squamous cell carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is the prominent cancer in head and neck, which greatly affects life quality of patients. The pathogenesis of LSCC is not clear. Presently, the LSCC treatments include chemotherapy, surgery and radiotherapy; however, these methods have poor efficacy in patients with recurrent and persistent cancer. Therefore, the study identified the hub genes accompanied with LSCC, which may be a potential therapeutic target in the future.

Methods

We extracted whole transcriptome high-throughput sequencing (HTS) LSCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and calculate differentially expressed genes (DEGs) between LSCC and normal samples using statistical software RStudio. Through weighted gene co-expression network analysis (WGCNA), enrichment examination of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) functions, and examination of protein-protein interaction (PPI) network, we obtained network hub genes and validated the hub genes prognostic value and expression levels of protein.

Results

Through analysis of differential gene expression, from the GEO and TCGA databases 2,139 and 2,774 DEGs were obtained, respectively, 13 and 15 modules were screened from TCGA-LSCC and GSE127165 datasets by WGCNA, respectively. The most significant positive and negative correlation modules in the WGCNA and DEG lists were overlapped, and overall 36 co-expressed overlapping genes were retrieved. Through enrichment analysis of GO and KEGG, it was found that the gene functions were highly concentrated in cell junction assembly, basement membrane, extracellular matrix (ECM) structural constituent etc., and the pathways were mainly concentrated in ECM receptor interaction, focal adhesion, small cell lung cancer, and toxoplasmosis. Through analysis of PPI network analysis, 10 network hub genes (SNAI2, ITGA6, LAMB3, LAMC2, CAV1, COL7A1, GJA1, EHF, OAT, and GPT) were obtained. Finally, survival analysis and protein expression validation of these genes confirmed that low OAT expression and high CAV1 expression remarkably influenced the survival of patient's prognosis with LSCC.

Conclusions

We recognized the hub genes and key modules nearly associated to LSCC and these genes were validated by survival analysis and the database of Human Protein Atlas (HPA), which is of high importance for unveiling the pathogenesis of LSCC and probing for new precise biological marker and potential therapeutic targets.

The Role of Lipid Metabolism Disorders in the Development of Thyroid Cancer

Thyroid cancer (TC) is a neoplasm with an increasing incidence worldwide. Its etiology is complex and based on a multi-layered interplay of factors. Among these, disorders of lipid metabolism have emerged as an important area of investigation. Cancer cells are metabolically reprogrammed to promote their rapid growth, proliferation, and survival. This reprogramming is associated with significant changes at the level of lipids, mainly fatty acids (FA), as they play a critical role in maintaining cell structure, facilitating signaling pathways, and providing energy. These lipid-related changes help cancer cells meet the increased demands of continued growth and division while adapting to the tumor microenvironment. In this review, we examine lipid metabolism at different stages, including synthesis, transport, and oxidation, in the context of TC and the effects of obesity and hormones on TC development. Recent scientific efforts have revealed disturbances in lipid homeostasis that are specific to thyroid cancer, opening up potential avenues for early detection and targeted therapeutic interventions. Understanding the intricate metabolic pathways involved in FA metabolism may provide insights into potential interventions to prevent cancer progression and mitigate its effects on surrounding tissues.

Effect of CHRDL1 on angiogenesis and metastasis of colorectal cancer cells via TGF-β/VEGF pathway

Colorectal cancer (CRC) is a common digestive tract tumor with the third incidence and death in the world. There is still an urgent need for effective therapeutic targets and prognostic markers for CRC. Herein, we report a novel potential target and marker, Chordin like-1 (CHRDL1). The function of CHRDL1 has been reported in gastric cancer, breast cancer, and oral squamous cell carcinoma. However, the biological effect of CHRDL1 in CRC remains unrevealed. Transwell and tube formation experiments were used to determine the biological function of CHRDL1. Western blot and rescue experiments were used to determine the specific mechanisms of CHRDL1. Results showed CHRDL1 is significantly downregulated in CRC cell lines and tissues. In vitro, experiments confirmed that CHRDL1 can inhibit cell growth, migration, invasion, angiogenesis and reverse epithelial-mesenchymal transformation. In vivo, experiments proved that it can inhibit tumor growth and metastasis. Mechanistically, we newly find that CHRDL1 exerts biological functions through the transforming growth factor-beta (TGF-β)/vascular endothelial growth factor signaling axis in vitro and in vivo. Therefore, we concluded that CHRDL1 reduces the growth, migration, and angiogenesis of CRC cells by downregulating TGF-β signaling. Our new findings on CHRDL1 may provide a basis for clinical antiangiogenesis therapy and the prognosis of CRC.

An individualized protein-based prognostic model to stratify pediatric patients with papillary thyroid carcinoma

Pediatric papillary thyroid carcinomas (PPTCs) exhibit high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collect and evaluate the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantify 10,426 proteins by mass spectrometry. We find 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses show the enhanced activation of the inflammatory and immune system in PM patients compared with the others. Nineteen proteins are selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generates a protein-based personalized prognostic prediction model that can stratify PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.

Comparative Cyto-Histological Genetic Profile in a Series of Differentiated Thyroid Carcinomas

INTRODUCTION

Molecular tests can contribute to improve the preoperative diagnosis of thyroid nodules. Tests available are expensive and not adapted to different populations.

AIM

This study aimed to compare the cyto-histological genetic profile and to evaluate the reliability of molecular tests using ultrasound-guided fine needle aspiration cytology (US-FNAC) in accurately diagnosing differentiated thyroid carcinomas (DTCs) and predicting biologic behavior of papillary thyroid carcinomas (PTCs).

MATERIALS AND METHODS

The series included 259 patients with paired cyto-histological samples totaling 518 samples. The genetic alterations were analyzed via PCR/Sanger sequencing. The association with clinicopathologic features was evaluated in PTCs.

RESULTS/DISCUSSION

From the 259 patients included, histologies were 50 (19.3%) benign controls and 209 (80.7%) DTC cases, from which 182 were PTCs; cytologies were 5.8% non-diagnostic, 18.2% benign, 39% indeterminate, and 37.1% malignant. In histology, indeterminate nodules (n = 101) were 22.8% benign and 77.2% malignant. Mutation frequencies in cytology and histology specimens were, respectively, TERTp: 3.7% vs. 7.9%; BRAF: 19.5% vs. 25.1%; and RAS: 11% vs. 17.5%. The overall cyto-histological agreement of the genetic mutations was 94.9%, with Cohen's k = 0.67, and in indeterminate nodules agreement was 95.7%, k = 0.64. The identified mutations exhibited a discriminative ability in diagnosing DTC with a specificity of 100% for TERTp and BRAF, and of 94% for RAS, albeit with low sensitivity. TERTp and BRAF mutations were associated with aggressive clinicopathological features and tumor progression in PTCs (p < 0.001). The obtained good cyto-histological agreement suggests that molecular analysis via US-FNAC may anticipate the genetic profile and the behavior of thyroid tumors, confirming malignancy and contributing to referring patients to surgery.

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

PRMT1 accelerates cell proliferation, migration, and tumor growth by upregulating ZEB1/H4R3me2as in thyroid carcinoma

Thyroid carcinoma (TC) represents the most prevalent malignancy of the endocrine system. Protein arginine methyltransferase 1 (PRMT1) is a critical member of the protein arginine methyltransferase family in mammals and is involved in multiple biological processes. This study aimed to investigate the function of PRMT1 in TC. In the present study, human TC cell lines (8505C, CAL62, and BCPAP) and a normal human thyroid cell line Nthy‑ori 3‑1 were employed. Small interfering RNA targeting PRMT1 was used to knock down PRMT1 expression in 8505C cells, and PRMT1 overexpression plasmids were transfected into BCPAP cells. Cell viability was assessed using a CCK‑8 and colony formation assays. Apoptosis was measured using flow cytometry and TUNEL assays. Cell migration was assessed using wound healing and Transwell assays. Reverse transcription‑quantitative PCR was used to determine the mRNA expression levels of PRMT1. Western blotting was used to detect the protein expression levels of PRMT1, E‑cadherin, vimentin, H4R3me2as, and zinc‑finger E homeobox‑binding 1 (ZEB1). Notably, PRMT1 expression was elevated in TC (P<0.01). PRMT1 knockdown inhibited TC cell proliferation and migration and concurrently enhanced migration. Furthermore, PRMT1 knockdown suppressed tumor growth and metastasis in a mouse model of TC. PRMT1 downregulation increased E‑cadherin expression and decreased the expression of vimentin, H4R3me2as, and ZEB1 in the TC cells and the mouse model of TC. Conversely, PRMT1 overexpression had the opposite effect on TC malignant characteristics (P<0.05). These findings suggest that PRMT1 knockdown inhibited TC malignancy by downregulating H4R3me2as/ZEB1, thereby highlighting novel therapeutic targets and diagnostic markers for the management of TC.

Bioinformatics identify the role of chordin-like 1 in thyroid cancer

The abnormal expression of chordin-like 1 (CHRDL1) is identified in many cancers, while the effect of CHRDL1 in thyroid cancer (THCA) remains unclear. The University of California Santa Cruz, Gene Expression Profiling Interactive Analysis, University of Alabama at Birmingham Cancer, and Gene Expression Omnibus database (GSE33570, GSE33630, and GSE60542) were used for determining the mRNA and methylation expression of CHRDL1 in tumor and normal tissues. Human Protein Atlas was used for exploring the protein expression level of CHRDL1. The genes correlated to CHRDL1 were assessed by cBioPortal database. The prognostic value of CHRDL1 was evaluated through Kaplan-Meier method, cox regression, and nomogram analysis. Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and gene set enrichment analysis were used for predicting potential function of CHRDL1. The relationship between CHRDL1 and immune cell infiltration was determined by Pearson method. The downregulated mRNA and protein expressions of CHRDL1 were identified in THCA through the analysis of data from The Cancer Genome Atlas, Gene Expression Omnibus, and Human Protein Atlas database. The survival analysis showed that the CHRDL1 expression significantly affected disease-free interval (DFI) and progression-free interval, and CHRDL1 was an independent predictor of DFI. Besides, we found that C-C motif chemokine ligand 21 could significantly affect DFI time when it was co-expressed with CHRDL1. Additionally, the function of CHRDL1 was enriched in cell migration, apoptosis, and immune cell receptor. The downregulated expression of CHRDL1 was observed in THCA and caused poor prognosis. CHRDL1 may be involved in signal pathway related to cancer development and immune response, which suggested it could be a potential biomarker.

Weighted gene co-expression network analysis and CIBERSORT screening of key genes related to m6A methylation in Hirschsprung's disease

Hirschsprung's disease (HSCR) is a neural crest disease that results from the failure of enteric neural crest cells (ENCCs) to migrate to the corresponding intestinal segment. The RET gene, which regulates enteric neural crest cell proliferation and migration, is considered one of the main risk factors for HSCR and is commonly used to construct HSCR mouse models. The epigenetic mechanism of m6A modification is involved in HSCR. In this study, we analyzed the GEO database (GSE103070) for differentially expressed genes (DEGs) and focused on m6A-related genes. Comparing the RNA-seq data of Wide Type and RET Null, a total of 326 DEGs were identified, of which 245 genes were associated with m6A. According to the CIBERSORT analysis, the proportion of Memory B-cell in RET Null was significantly higher than that of Wide Type. Venn diagram analysis was used to identify key genes in the selected memory B-cell modules and DEGs associated with m6A. Enrichment analysis showed that seven genes were mainly involved in focal adhesion, HIV infection, actin cytoskeleton organization and regulation of binding. These findings could provide a theoretical basis for molecular mechanism studies of HSCR.

Identification of Two CDK5R1-Related Subtypes and Characterization of Immune Infiltrates in Alzheimer's Disease Based on an Integrated Bioinformatics Analysis

Background

Alzheimer's disease (AD) is a neurodegenerative disorder and the major cause of senile dementia. The Reelin pathway has been involved in both learning and AD pathogenesis. However, the specific Reelin-related gene signature during the pathological process remains unknown.

Methods

Reelin-related gene (CDK5R1) expression was analyzed using the GEO datasets. The relevant genes of CDK5R1 were identified using differential expression analysis and weighted gene correlation network analysis (WGCNA) based on the GSE43850 dataset. ConsensusClusterPlus analysis was applied to identify subtypes (C1 and C2) of AD. The CIBERSORT algorithm was used to assess the immune cell infiltration between the two AD subtypes.

Results

CDK5R1 was downregulated in AD. 244 differentially expressed CDK5R1-related genes (DECRGs) between the two subgroups were mainly enriched in GABAergic synapse, neuroactive ligand-receptor interaction, synapse organization, neurotransmitter transport, etc. Furthermore, the GSVA results indicated that immune-related pathways were significantly enriched in the C1 subgroup. Interestingly, 10 Reelin pathway-related genes (CRK, DAB2IP, LRP8, RELN, STAT5A, CDK5, CDK5R1, DAB1, FYN, and SH3KBP1) were abnormally expressed between the two subgroups. The proportion of T cell gamma delta, monocytes, macrophage M2, and dendritic cells activated decreased from C1 to C2, while the proportion of plasma cells, T cell follicular helper, and NK cells activated increased.

Conclusion

Two CDK5R1-related subtypes of AD were identified, helping us to better understand the role of CDK5R1 in the pathological process of AD.

Diagnostic, Prognostic, and Immunological Roles of FABP4 in Pancancer: A Bioinformatics Analysis

BACKGROUND

Fatty acid binding protein 4 (FABP4) is mainly involved in the regulation of systemic metabolism through various lipid signaling pathways. Metabolic reprogramming is one of the important factors in the development and progression of cancer. It has been recently reported that FABP4 is closely related to the development of cancer and may be involved in tumor invasion and metastasis.

METHODS

In this study, we explored the expression pattern of FABP4 in pancancer through TCGA and CPTAC. Using TCGA, Kaplan-Meier Plotter, and STRING databases, to explore its diagnostic and prognostic value, and function through GO/KEGG and GSEA. Then, using the TIMER2.0 database, we investigated the correlation between FABP4 expression and immune infiltration in cancers, especially stomach adenocarcinomas (STAD) and colorectal adenocarcinoma (COADREAD).

RESULTS

Compared with normal tissues, the expression of FABP4 in more than 10 tumor tissues was lower (p < 0.05). Through the receiver operating characteristic (ROC) curve, the diagnostic value was found higher in colorectal cancer, breast cancer, thyroid cancer, and lung cancer, with the area under the curve (AUC) > 0.9. Through the K-M curve, FABP4 was found to correlate to the prognosis of various cancers. The results of gastric cancer and colorectal cancer are consistent. The low-expression group has a better prognosis than the high-expression group, and the expression of FABP4 in the early T and N stages of gastrointestinal tumors is lower. FABP4 highly expressed gene set is mostly enriched in extracellular matrix degradation and cell adhesion functions. Gastrointestinal tumors with high expression of FABP4 may have more immunosuppressive effects on macrophages and have a worse prognosis.

CONCLUSION

FABP4 can be used as a diagnostic and prognostic biomarker in pancancer, and its high expression in gastrointestinal tumors suggests poor prognosis. This may be correlated to the immune infiltration of macrophages and epithelial-mesenchymal transition.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning

OBJECTIVES

To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers.

METHODS

Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs.

RESULTS

Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726).

CONCLUSIONS

PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

BACKGROUND

To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

METHODS

In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

RESULTS

We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

CONCLUSION

Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

Bioinformatics-Based Approach for Exploring the Immune Cell Infiltration Patterns in Alzheimer's Disease and Determining the Intervention Mechanism of Liuwei Dihuang Pill

Traditional Chinese medicine (TCM) compounds have recently garnered attention for the regulation of immune cell infiltration and the prevention and treatment of Alzheimer's disease (AD). The Liuwei Dihuang Pill (LDP) has potential in this regard; however, its specific molecular mechanism currently remains unclear. Therefore, we adopted a bioinformatics approach to investigate the infiltration patterns of different types of immune cells in AD and explored the molecular mechanism of LDP intervention, with the aim of providing a new basis for improving the clinical immunotherapy of AD patients. We found that M1 macrophages showed significantly different degrees of infiltration between the hippocampal tissue samples of AD patients and healthy individuals. Four immune intersection targets of LDP in the treatment of AD were identified; they were enriched in 206 biological functions and 30 signaling pathways. Quercetin had the best docking effect with the core immune target PRKCB. Our findings suggest that infiltrated immune cells may influence the course of AD and that LDP can regulate immune cell infiltration through multi-component, multi-target, and multi-pathway approaches, providing a new research direction regarding AD immunotherapy.

Sex-Specific Transcriptomic Signatures in Brain Regions Critical for Neuropathic Pain-Induced Depression

Neuropathic pain is a chronic debilitating condition with a high comorbidity with depression. Clinical reports and animal studies have suggested that both the medial prefrontal cortex (mPFC) and the anterior cingulate cortex (ACC) are critically implicated in regulating the affective symptoms of neuropathic pain. Neuropathic pain induces differential long-term structural, functional, and biochemical changes in both regions, which are thought to be regulated by multiple waves of gene transcription. However, the differences in the transcriptomic profiles changed by neuropathic pain between these regions are largely unknown. Furthermore, women are more susceptible to pain and depression than men. The molecular mechanisms underlying this sexual dimorphism remain to be explored. Here, we performed RNA sequencing and analyzed the transcriptomic profiles of the mPFC and ACC of female and male mice at 2 weeks after spared nerve injury (SNI), an early time point when the mice began to show mild depressive symptoms. Our results showed that the SNI-induced transcriptomic changes in female and male mice were largely distinct. Interestingly, the female mice exhibited more robust transcriptomic changes in the ACC than male, whereas the opposite pattern occurred in the mPFC. Cell type enrichment analyses revealed that the differentially expressed genes involved genes enriched in neurons, various types of glia and endothelial cells. We further performed gene set enrichment analysis (GSEA), which revealed significant de-enrichment of myelin sheath development in both female and male mPFC after SNI. In the female ACC, gene sets for synaptic organization were enriched, and gene sets for extracellular matrix were de-enriched after SNI, while such signatures were absent in male ACC. Collectively, these findings revealed region-specific and sexual dimorphism at the transcriptional levels induced by neuropathic pain, and provided novel therapeutic targets for chronic pain and its associated affective disorders.

Study on the expression and function of chordin like 1 in oral squamous cell carcinoma

OBJECTIVE

The aim of this study was to investigate the role and related mechanism of chordin like 1 (CHRDL1) in oral squamous cell carcinoma (OSCC).

METHODS

The expressions of CHRDL1 were analyzed in both mRNA and protein levels by bioinformatics analysis, immunohistochemistry, and fluorescence in situ hybridization in OSCC. Survival analysis was used to determine the relationship between CHRDL1 and prognosis. In addition, enrichment analysis was used to suggest signal pathways involved in CHRDL1. Besides, the relationships between CHRDL1 and miRNAs, hypoxia, and immune infiltration were explored.

RESULTS

The mRNA level of CHRDL1 in OSCC was significantly lower than that in normal tissues, while the protein level was significantly higher than that in normal tissues. The high mRNA levels of CHRDL1 suggested a poor prognosis in patients with OSCC. The enrichment results showed that CHRDL1 might be involved in the Calcium signaling pathway, dilated cardiomyopathy, and focal adhesion. 7 immune cells were positively correlated with CHRDL1, while Tgd was negatively correlated with CHRDL1. In addition, we also found that hsa-miR-455-3p directly targeted CHRDL1 and reduced the mRNA levels of CHRDL1.

CONCLUSION

CHRDL1 plays a vital role in promoting cancer in OSCC and is downregulated at the mRNA levels by hsa-miR-455-3p.

GATA binding protein 1 recruits histone deacetylase 2 to the promoter region of nuclear receptor binding protein 2 to affect the tumor microenvironment and malignancy of thyroid carcinoma

The tumor microenvironment (TME) and activated angiogenesis in thyroid carcinoma (TC) are critical for tumor growth and metastasis. Nuclear receptor binding protein 2 (NRBP2) has been suggested as a tumor suppressor. This study examines the function of NRBP2 in the progression of TC and the regulatory mechanism. By analyzing bioinformatic tools including GSE165724 dataset and the Cancer Genome Atlas system, we predicted NRBP2 as a poorly expressed gene in TC. Decreased NRBP2 expression was detected in TC tumor tissues and cells. Poor expression of NRBP2 was linked to unfavorable prognosis of patients. GATA binding protein 1 (GATA1) was found as a negative regulator of NRBP2. It recruited histone deacetylase2 (HDAC2) to the NRBP2 promoter to trigger histone deacetylation. NRBP2 overexpression suppressed growth of TC cells, and it reduced expression of TME markers, M2 polarization of macrophages, and angiogenesis in TC. Similar results were reproduced in vivo in nude mice. However, the anti-oncogenic roles of NRBP2 were blocked after further overexpression of GATA1 or HDAC2. In summary, this study demonstrates that GATA1 recruits HDAC2 to the NRBP2 promoter and enhances the TME and angiogenesis in TC cells.

Chordin-Like 1 Regulates Epithelial-to-Mesenchymal Transition and Metastasis via the MAPK Signaling Pathway in Oral Squamous Cell Carcinoma

Background

Accumulating evidence suggests that dysregulation of Chordin-like 1 (CHRDL1) is associated with malignant biological behaviors in multiple cancers. However, the exact function and molecular mechanism of CHRDL1 in oral squamous cell carcinoma (OSCC) remain unclear.

Methods

The expression levels of CHRDL1 in OSCC tissues and CAL27 cells were determined by RT-qPCR. Immunohistochemical staining was applied to detect CHRDL1 protein expression in sample tissues from OSCC patients. Gain of function and knockdown by lentivirus were further used to examine the effects of CHRDL1 on cell proliferation, migration, invasion, and adhesion in OSCC. Tail vein injection of CAL27 cells with dysregulated CHRDL1 expression was further used to examine the effect of CHRDL1 on lung colonization. RNA sequencing was performed to explore the molecular mechanisms of CHRDL1 that underlie the progression of OSCC.

Results

CHRDL1 was significantly downregulated in OSCC tissues and CAL27 cells compared to controls. CHRDL1 knockdown enhanced migration, invasion, adhesion, and EMT, but not proliferation, in CAL27 cells. Overexpression of CHRDL1 had the opposite effects. Moreover, CHRDL1 was proven to inhibit tumor metastasis in vivo. Mechanistically, MAPK signaling pathway components, including ERK1/2, p38, and JNK, were found to regulate the malignant biological behaviors of CAL27 cells.

Conclusions

Our results suggest that CHRDL1 has an inhibitory effect on OSCC metastasis via the MAPK signaling pathway, which provides a new possible potential therapeutic target against OSCC.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

Identification of a Four-Gene Signature for Determining the Prognosis of Papillary Thyroid Carcinoma by Integrated Bioinformatics Analysis

Purpose

Although well-differentiated papillary thyroid carcinoma (PTC) has an indolent nature and usually an excellent prognosis, some patients experience disease recurrence or death. The aim of this study was to identify prognostic markers to stratify PTC patients.

Patients and Methods

Eight gene-expression profiles (GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE53157, GSE60542, and GSE104005) were obtained from the Gene Expression Omnibus and used to analyze differentially expressed genes (DEGs) between PTC tissues and non-tumor tissues. Univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed to identify prognostic genes and establish a risk-score model based on the integrated DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were used to validate the prognostic performance of the risk score. A nomogram was constructed based on The Cancer Genome Atlas dataset and Multivariable Cox regression analysis.

Results

A total of 165 upregulated and 207 downregulated DEGs were screened. A four-gene signature including PAPSS2, PCOLCE2, PTX3, and TGFBR3 was identified. The risk-score model showed a strong diagnosis performance for identifying patients with a poor prognosis. KM analysis showed that patients with low risk scores had a significantly more favorable overall survival (OS) than those with high risk scores (p = 0.0002). ROC curves based on the four-gene signature showed better performances in predicting 1-, 3-, and 5-year survival than did the American Joint Committee on Cancer staging system (area under the curve: 0.86 vs 0.84, 0.80 vs 0.63, and 0.79 vs 0.73, respectively). Furthermore, when combined with age and tumor status from the nomogram, the four-gene signature achieved a good performance in guiding postoperative follow-up surveillance of patients with PTC.

Conclusion

The four-gene signature was found to be a novel and reliable biomarker with great potential for clinical application in risk stratification and OS prediction in patients with PTC.

Identification of Hub Genes and Immune Cell Infiltration Characteristics in Alzheimer's Disease

The purpose of this study was to identify hub genes closely correlated with Alzheimer's disease (AD) and their association with immune cell infiltration. In this work, 119 overlapping differentially expressed genes (DEGs) were obtained from GSE5281 and GSE122063 datasets through differential expression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the 119 DEGs, revealing some important biological functions and key pathways. AD immune cell infiltration analysis revealed a significant difference in the proportion of immune cells between the AD group and the control group. Finally, correlation analysis between target hub genes and immune cells indicated that GFAP had a positive or negative correlation with some specific immune cells. Our results provided useful clues, which will help to explain the molecular mechanism of AD and search for precise prognostic markers and potential therapeutic targets.