Identification of gene co-expression modules and hub genes associated with lymph node metastasis of papillary thyroid cancer

MET-receptor targeted fluorescent imaging and spectroscopy to detect multifocal papillary thyroid cancer

PURPOSE

Multifocal disease in PTC is associated with an increased recurrence rate. Multifocal disease (MD) is underdiagnosed with the current gold standard of pre-operative ultrasound staging. Here, we evaluate the use of EMI-137 targeted molecular fluorescence-guided imaging (MFGI) and spectroscopy as a tool for the intra-operative detection of uni- and multifocal papillary thyroid cancer (PTC) aiming to improve disease staging and treatment selection.

METHODS

A phase-1 study (NCT03470259) with EMI-137 was conducted to evaluate the possibility of detecting PTC using MFGI and quantitative fiber-optic spectroscopy.

RESULTS

Fourteen patients underwent hemi- or total thyroidectomy (TTX) after administration of 0.09 mg/kg (n = 1), 0.13 mg/kg (n = 8), or 0.18 mg/kg (n = 5) EMI-137. Both MFGI and spectroscopy could differentiate PTC from healthy thyroid tissue after administration of EMI-137, which binds selectively to MET in PTC. 0.13 mg/kg was the lowest dosage EMI-137 that allowed for differentiation between PTC and healthy thyroid tissue. The smallest PTC focus detected by MFGI was 1.4 mm. MFGI restaged 80% of patients from unifocal to multifocal PTC compared to ultrasound.

CONCLUSION

EMI-137-guided MFGI and spectroscopy can be used to detect multifocal PTC. This may improve disease staging and treatment selection between hemi- and total thyroidectomy by better differentiation between unifocal and multifocal disease.

TRIAL REGISTRATION

NCT03470259.

PERP May Affect the Prognosis of Lung Adenocarcinoma by Inhibiting Apoptosis

Background

PERP, a member of the peripheral myelin protein gene family, is a new therapeutic target in cancer. The relationships between PERP and immune cell infiltration in lung cancer have not been studied. Therefore, the role of PERP in the tumour microenvironment (TME) of lung cancer needs to be further explored.

Methods

In this study, we explored the association between PERP expression and clinical characteristics by analysing data from the TCGA database. Cox regression and Kaplan‒Meier methods were used to investigate the relationship between the expression of PERP and overall survival in patients with lung adenocarcinoma (LUAD). The relationship between PERP expression and the degree of infiltration of specific immune cell subsets in LUAD was evaluated using the TIMER database and GEPIA. We also performed GO enrichment analysis and KEGG enrichment analysis to reveal genes coexpressed with PERP using the Coexpedia database. Finally, we verified the expression and function of PERP in LUAD tissues and the A549 cell line by RT‒PCR, Western blot, CCK-8, IHC, and wound healing assays. The mouse model was used to study the in vivo effects of PERP.

Results

According to our results, PERP expression was significantly higher in LUAD tissues and associated with the clinical characteristics of the disease. Survival was independently associated with PERP in LUAD patients. We further verified that PERP might regulate B-cell infiltration in LUAD to affect the prognosis of LUAD. To identify PERP-related signalling pathways in LUAD, we performed a genome-aggregation analysis (GSEA) between low and high PERP expression datasets. LUAD cells express higher levels of PERP than paracarcinoma cells, and PERP inhibits the proliferation and metastasis of A549 cells through apoptosis.

Conclusion

PERP may affect the prognosis of lung adenocarcinoma by inhibiting apoptosis and is associated with immune cell infiltration.

A Potential Prognostic Gene Signature Associated with p53-Dependent NTRK1 Activation and Increased Survival of Neuroblastoma Patients

Neuroblastoma is the most common extracranial solid tumour in children, comprising close to 10% of childhood cancer-related deaths. We have demonstrated that activation of NTRK1 by TP53 repression of PTPN6 expression is significantly associated with favourable survival in neuroblastoma. The molecular mechanisms by which this activation elicits cell molecular changes need to be determined. This is critical to identify dependable biomarkers for the early detection and prognosis of tumours, and for the development of personalised treatment. In this investigation we have identified and validated a gene signature for the prognosis of neuroblastoma using genes differentially expressed upon activation of the NTRK1-PTPN6-TP53 module. A random survival forest model was used to construct a gene signature, which was then assessed across validation datasets using Kaplan-Meier analysis and ROC curves. The analysis demonstrated that high BASP1, CD9, DLG2, FNBP1, FRMD3, IL11RA, ISGF10, IQCE, KCNQ3, and TOX2, and low BSG/CD147, CCDC125, GABRB3, GNB2L1/RACK1 HAPLN4, HEBP2, and HSD17B12 expression was significantly associated with favourable patient event-free survival (EFS). The gene signature was associated with favourable tumour histology and NTRK1-PTPN6-TP53 module activation. Importantly, all genes were significantly associated with favourable EFS in an independent manner. Six of the signature genes, BSG/CD147, GNB2L1/RACK1, TXNDC5, FNPB1, B3GAT1, and IGSF10, play a role in cell differentiation. Our findings strongly suggest that the identified gene signature is a potential prognostic biomarker and therapeutic target for neuroblastoma patients and that it is associated with neuroblastoma cell differentiation through the activation of the NTRK1-PTPN6-TP53 module.

Integrated gene profiling of fine-needle aspiration sample improves lymph node metastasis risk stratification for thyroid cancer

BACKGROUND

Lymph node metastasis risk stratification is crucial for the surgical decision-making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine-Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer.

METHODS

In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi-omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models.

RESULTS

One hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016-2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80-0.94)] than either expression classifier [AUC = 0.67 (0.61-0.74)], mutation classifier [AUC = 0.61 (0.55-0.67)] or TIRADS score [AUC = 0.68 (0.62-0.74)] with statistical significance (p < 0.001), and the IRS model had similar predictive performance in large nodule [>1 cm, AUC = 0.88 (0.79-0.97)] and small nodule [≤1 cm, AUC = 0.84 (0.74-0.93)] subgroups. The genetic risk factor showed independent predictive value (OR = 10.3, 95% CI:1.1-105.3) of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size.

CONCLUSION

The integrated gene profiling of FNA samples and the IRS model developed by the machine-learning method significantly improve the risk stratification of thyroid cancer, thus helping make wise decisions and reducing unnecessary extensive surgeries.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

A Combined Risk Score Model to Assess Prognostic Value in Patients with Soft Tissue Sarcomas

A study by Tsvetkov et al. recently published a proposed novel form of copper-induced cell death in Science; however, few studies have looked into the possible mechanism in soft tissue sarcoma (STS). Herein, this study sought to investigate the function of cuproptosis-related genes (CRGs) in the development of tumor-associated immune cells and the prognosis of sarcoma. Herein, this study aimed to explore the role of cuproptosis-related genes (CRGs) in the development, tumor-associated immune cells, and the prognosis of sarcoma.

METHODS

The prognostic model was established via the least absolute shrinkage and selection operator (LASSO) algorithm as well as multivariate Cox regression analysis. The stromal scores, immune scores, ESTIMA scores, and tumor purity of sarcoma patients were evaluated by the ESTIMATE algorithm. Functional analyses were performed to investigate the underlying mechanisms of immune cell infiltration and the prognosis of CRGs in sarcoma.

RESULTS

Two molecular subgroups with different CRG expression patterns were recognized, which showed that patients with a higher immune score and more active immune status were prone to have better prognostic survival. Moreover, GO and KEGG analyses showed that these differentially expressed CRGs were mainly enriched in metabolic/ions-related signaling pathways, indicating that CRGs may have impacts on the immune cell infiltration and prognosis of sarcoma via regulating the bioprocess of mitochondria and consequently affecting the immune microenvironment. The expression levels of CRGs were closely correlated to the immunity condition and prognostic survival of sarcoma patients.

CONCLUSIONS

The interaction between cuproptosis and immunity in sarcoma may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for sarcoma patients.

Weighted gene co-expression network reveals driver genes contributing to phenotypes of anaplastic thyroid carcinoma and immune checkpoint identification for therapeutic targets

Background

Anaplastic thyroid carcinoma (ATC) is a rare but extremely malignant tumor, with a rapid growth rate and early metastasis thus leading to poor survival of patients. The molecular mechanisms underlying these aggressive traits of ATC remain unknown, which impedes the substantial progress in treatment to prolong ATC patient survival.

Methods

We applied weighted gene co-expression network analysis (WGCNA) to identify ATC-specific modules. The Metascape web and R package clusterProfiler were employed to perform enrichment analysis. Combined with differentially expressed gene analysis, we screened out the most potential driver genes and validated them using receiver operator characteristic (ROC) analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry (IHC), and triple immunofluorescence staining.

Results

A gene expression matrix covering 75 normal samples, 83 papillary thyroid carcinoma (PTC), 26 follicular thyroid carcinoma (FTC), 19 poor-differentiated thyroid carcinoma (PDTC), and 41 ATC tissue samples were integrated, based on which we detected three most potential ATC-specific modules and found that hub genes of these modules were enriched in distinct biological signals. Hub genes in the turquoise module were mainly enriched in mitotic cell cycle, tube morphogenesis, and cell differentiation, hub genes in the magenta module were mainly clustered in the extracellular matrix organization, positive regulation of cell motility, and regulation of Wnt signaling pathway, while hub genes in the blue module primarily participated in the inflammatory response, innate immune response, and adaptive immune response. We showed that 9 top genes, 8 transcription factors (TFs), and 4 immune checkpoint genes (ICGs) were differentially expressed in ATC compared to other thyroid samples and had high diagnostic values for ATC, among which, 9 novel ATC-specific genes (ADAM12, RNASE2, CASP5, KIAA1524, E2F7, MYBL1, SRPX2, HAVCR2, and TDO2) were validated with our clinical samples. Furthermore, we illustrated that ADAM12, RNASE2, and HAVCR2 were predominantly present in the cytoplasm.

Conclusion

Our study identified a set of novel ATC-specific genes that were mainly related to cell proliferation, invasion, metastasis, and immunosuppression, which might throw light on molecular mechanisms underlying aggressive phenotypes of ATC and provide promisingly diagnostic biomarkers and therapeutic targets.

Construction of a Cuprotosis-Related Gene-Based Model to Improve the Prognostic Evaluation of Patients with Gastric Cancer

Background

Gastric cancer (GC) is one of the most serious gastrointestinal malignancies with bad prognosis. The association between GC and cuprotosis-related genes has not been reported.

Methods

The clinical and RNA expression of patients with GC were downloaded from TCGA database. The CIBERSORT package was used to quantify the abundance of specific cell types. Using the Cox regression analysis, we conducted a prognostic nomogram model based on cuprotosis-related differential genes in GC. We evaluated the prognostic power of this model using the Kaplan-Meier (K-M) survival curve analysis, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis.

Results

The plasma cells, monocytes, and mast cells in GC tissue were significantly less than those in adjacent tissue (p < 0.05), while T cell CD4 memory activated macrophage M0, macrophage M1, and macrophages in GC tissue. The number of M2 was significantly more than that in the adjacent tissue (p < 0.05). Additionally, GC patients in the test group, the training group, and all the sample groups had shorter survival time with the increase of the risk factor (p < 0.05). The nomogram of GC based on cuprotosis prognosis-related genes was conducted. The AUC of the nomogram to predict 1-, 3-, and 5-year survival rate was 0.618, 0.618, and 0.625, respectively.

Conclusion

A novel cuprotosis-related gene signature impacts on the prognosis of GC. Our research provides new insights and potential targets for studying the link between GC and cuprotosis point, thereby providing new insights into understanding the molecular mechanism of GC.

Insulin receptor substrate 1(IRS1) is related with lymph node metastases and prognosis in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the leading causes of cancer-related mortality globally with a high risk of lymph node metastasis (LNM). In this study, weighted gene co-expression network analysis (WGCNA) showed the identification of 10 modules among which the significant module (turquoise), including 1352 genes, was correlated with LNM. A group 52 overlapping differentially expressed genes (DEGs) was identified based on the comparison of turquoise module with GSE23400 and GSE20347 datasets. Using Ctyohubba plugin, we identified 7 hub genes (ACTG2, SORBS1, MYH11, CXCL12, CNN1, IRS1 and CXCL8). IRS1 displayed significant correlation with metastasis. The decreased expression of IRS1 was also a predictor of poor OS of ESCC patients whereas the hub genes namely ACTG2, MYH11, CXCL8, CXCL12, IRS1 and CNN1 were associated with RFS of ESCC patients. These findings suggest that the altered expression of these hub genes are associated with prognosis and thus can be used as potential biomarkers for ESCC. Moreover, immunohistochemical staining and cell functional experiments displayed that the overexpression of IRS1 was negatively associated with metastasis in ESCC. In general, our research revealed several novel genes in ESCC especially the association of IRS1 with LNM in ESCC, which could provide novel insights into the initiation and progression of ESCC.

Intraoperative MET-receptor targeted fluorescent imaging and spectroscopy for lymph node detection in papillary thyroid cancer: novel diagnostic tools for more selective central lymph node compartment dissection

Purpose

Patients undergoing prophylactic central compartment dissection (PCLND) for papillary thyroid cancer (PTC) are often overtreated. This study aimed to determine if molecular fluorescence-guided imaging (MFGI) and spectroscopy can be useful for detecting PTC nodal metastases (NM) and to identify negative central compartments intraoperatively.

Methods

We used a data-driven prioritization strategy based on transcriptomic profiles of 97 primary PTCs and 80 normal thyroid tissues (NTT) to identify tumor-specific antigens for a clinically available near-infrared fluorescent tracer. Protein expression of the top prioritized antigen was immunohistochemically validated with a tissue microarray containing primary PTC (n = 741) and NTT (n = 108). Staining intensity was correlated with 10-year locoregional recurrence-free survival (LRFS). A phase 1 study (NCT03470259) with EMI-137, targeting MET, was conducted to evaluate safety, optimal dosage for detecting PTC NM with MFGI, feasibility of NM detection with quantitative fiber-optic spectroscopy, and selective binding of EMI-137 for MET.

Results

MET was selected as the most promising antigen. A worse LRFS was observed in patients with positive versus negative MET staining (81.9% versus 93.2%; p = 0.02). In 19 patients, no adverse events related to EMI-137 occurred. 0.13 mg/kg EMI-137 was selected as optimal dosage for differentiating NM from normal lymph nodes using MFGI (p < 0.0001) and spectroscopy (p < 0.0001). MFGI identified 5/19 levels (26.3%) without NM. EMI-137 binds selectively to MET.

Conclusion

MET is overexpressed in PTC and associated with increased locoregional recurrence rates. Perioperative administration of EMI-137 is safe and facilitates NM detection using MFGI and spectroscopy, potentially reducing the number of negative PCLNDs with more than 25%.

Clinical trial registration.

NCT03470259.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05763-3.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

In Silico Analysis of Ion Channels and Their Correlation with Epithelial to Mesenchymal Transition in Breast Cancer

Simple Summary

Breast cancer involves changes in the healthy cells of the breast resulting in rapid and abnormal division of cells that later spread to other parts of the body through the process of metastasis, which involves epithelial mesenchymal transition (EMT). Ion channels play a significant role in the switch from epithelial to mesenchymal transition through their contributions to cellular motility, cell volume regulation and cell cycle progression. Comprehensive computational analyses were performed to understand the role of ion channels in tumor/metastatic samples of breast cancer and their correlation with EMT.

Abstract

Uncontrolled growth of breast cells due to altered gene expression is a key feature of breast cancer. Alterations in the expression of ion channels lead to variations in cellular activities, thus contributing to attributes of cancer hallmarks. Changes in the expression levels of ion channels were observed as a consequence of EMT. Additionally, ion channels were reported in the activation of EMT and maintenance of a mesenchymal phenotype. Here, to identify altered ion channels in breast cancer patients, differential gene expression and weighted gene co-expression network analyses were performed using transcriptomic data. Protein–protein interactions network analysis was carried out to determine the ion channels interacting with hub EMT-related genes in breast cancer. Thirty-two ion channels were found interacting with twenty-six hub EMT-related genes. The identified ion channels were further correlated with EMT scores, indicating mesenchymal phenotype. Further, the pathway map was generated to represent a snapshot of deregulated cellular processes by altered ion channels and EMT-related genes. Kaplan–Meier five-year survival analysis and Cox regressions indicated the expression of CACNA1B, ANO6, TRPV3, VDAC1 and VDAC2 to be potentially associated with poor survival. Deregulated ion channels correlate with EMT-related genes and have a crucial role in breast cancer-associated tumorigenesis. Most likely, they are potential candidates for the determination of prognosis in patients with breast cancer.

DCAF13 promotes breast cancer cell proliferation by ubiquitin inhibiting PERP expression

Evolutionarily conserved DDB1-and CUL4-associated factor 13 (DCAF13) is a recently discovered substrate receptor for the cullin RING-finger ubiquitin ligase 4 (CRL4) E3 ubiquitin ligase that regulates cell cycle progression. DCAF13 is overexpressed in many cancers, although its role in breast cancer is currently elusive. In this study we demonstrate that DCAF13 is overexpressed in human breast cancer and that its overexpression closely correlates with poor prognosis, suggesting that DCAF13 may serve as a diagnostic marker and therapeutic target. We knocked down DCAF13 in breast cancer cell lines using CRISPR/Cas9 and found that DCAF13 deletion markedly reduced breast cancer cell proliferation, clone formation, and migration both in vitro and in vivo. In addition, DCAF13 deletion promoted breast cancer cell apoptosis and senescence, and induced cell cycle arrest in the G1/S phase. Genome-wide RNAseq analysis and western blotting revealed that loss of DCAF13 resulted in both mRNA and protein accumulation of p53 apoptosis effector related to PMP22 (PERP). Knockdown of PERP partially reversed the hampered cell proliferation induced by DCAF13 knockdown. Co-immunoprecipitation assays revealed that DCAF13 and DNA damage-binding protein 1 (DDB1) directly interact with PERP. Overexpression of DDB1 significantly increased PERP polyubiquitination, suggesting that CRL4^DCAF13 E3 ligase targets PERP for ubiquitination and proteasomal degradation. In conclusion, DCAF13 and the downstream effector PERP occupy key roles in breast cancer proliferation and potentially serve as prognostics and therapeutic targets.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

Aim

Methods

Results

Conclusions

ASF1B: A Possible Prognostic Marker, Therapeutic Target, and Predictor of Immunotherapy in Male Thyroid Carcinoma

Background

Thyroid carcinoma (TC) is the most common malignant endocrine tumor worldwide. Several studies have documented that male patients with TC have a higher rate of metastasis and disease recurrence than female patients. However, the mechanism underlying this observation is not completely clear. The goal of our research was to investigate the potential key candidate genes and pathways related to TC progression in male patients at the molecular level.

Methods

A total of 320 samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Hub genes were screened out using weighted gene coexpression network analysis (WGCNA) and a protein–protein interaction (PPI) network analysis. Survival analysis was used to identify hub genes associated with disease-free survival (DFS) rates. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression (ESTIMATE) data were used to assess the relationship between hub genes and immune cell infiltration. The molecular mechanism and biological functions of hub genes were explored using RT-qPCR, Western blot, Cell Counting Kit-8 Assay, flow cytometry, Transwell assays, and scratch assays.

Results

Forty-seven hub genes were identified, and the survival analysis demonstrated that anti-silencing function 1B (ASF1B) was the sole independent risk factor for poor DFS in male TC patients. Possible associations between the results from the ESTIMATE analysis showed that the ASF1B expression level was related to the ESTIMATE score, immune score, and T-cell regulatory (Treg) infiltration level. Through in vitro cell function experiments, we verified that knockdown of ASF1B inhibited KTC-1 cell proliferation, promoted cell apoptosis, and blocked cell cycle. The silencing of ASF1B reduced protein kinase B (AKT), phospho-AKT (p-AKT), and forkhead box p3 (FOXP3) in KTC-1 cells. Moreover, FOXP3 overexpression markedly restored the cell migration, invasion, and proliferation abilities repressed by ASF1B knockdown.

Conclusions

Our results indicate that ASF1B can be considered a prognostic marker, therapeutic target, and predictor of immunotherapy response in male thyroid cancer patients. However, further in-depth studies are required to validate this finding.

Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.

Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Background

Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors.

Methods

In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis.

Results

The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001).

Conclusions

The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

The NEAT1_2/miR-491 Axis Modulates Papillary Thyroid Cancer Invasion and Metastasis Through TGM2/NFκb/FN1 Signaling

NEAT1 (nuclear paraspeckle assembly transcript 1) is an oncogenic long non-coding RNA (lncRNA) that facilitates tumorigenesis in multiple cancers. In papillary thyroid cancer (PTC), the molecular mechanism by which NEAT1 affects invasion and metastasis remains elusive. RNA sequencing was used to discover differentially expressed NEAT1_2 downstream genes. Protein and RNA expression analyses and immunohistochemistry detected the expression of NEAT1_2, Transglutaminase 2 (TGM2), and microRNA-491 (miR-491) among PTC and non-cancerous tissues. Transwell and wound healing assays, and a mouse model of lung metastasis were used for further functional analyses. Bioinformatics was performed to predict miRNAs binding to both NEAT1_2 and TGM2. Rescue experiments and dual-luciferase reporter assays were performed. In PTC tissues, NEAT1_2 expression was markedly increased and regulated TGM2 expression. TGM2 was overexpressed in PTC, correlating positively with exthyroidal extension and lymph node metastasis. TGM2 knockdown significantly inhibited invasion and metastasis. NEAT1_2 sponged miR-491, acting as a competing endogenous RNA to regulate TGM2 expression. Fibronectin 1 (FN1) was predicted as a TGM2 target. TGM2 could transcriptionally activate FN1 by promoting nuclear factor kappa B (NFκb) p65 nuclear translocation, ultimately promoting PTC invasion/metastasis. These findings identify that NEAT1_2 sponges miR-491 to regulate TGM2 expression. TGM2 activates FN1 via NFκb to promote PTC invasion and metastasis.

Circular RNA circRUNX1 promotes papillary thyroid cancer progression and metastasis by sponging MiR-296-3p and regulating DDHD2 expression

Papillary thyroid cancer (PTC) has a continuously increasing incidence and imposes a heavy medical burden to individuals and society due to its high proportion of lymph node metastasis and recurrence in recent years. Circular RNAs, a class of noncoding RNAs, participate in the progression of many cancers, but the role of circRNAs in PTC is still rarely reported. In this study, circRNA deep sequencing was performed to identify differentially expressed circRNAs in PTC. CircRUNX1 was selected for its high expression in PTC, and circRUNX1 silencing was directly associated with the week potential for migration, invasion and proliferation of PTC in vivo and in vitro. Fluorescence in situ hybridization (FISH) was further used to confirm the cytoplasmic localization of circRUNX1, indicating the possible function of circRUNX1 as a ceRNAs in PTC progression through miRNA binding. MiR-296-3p was then confirmed to be regulated by circRUNX1 and to target DDHD domain containing 2 (DDHD2) by luciferase reporter assays. The strong antitumor effect of miR-296-3p and the tumor-promoting effect of DDHD2 were further investigated in PTC, indicating that circRUNX1 modulates PTC progression through the miR-296-3p/DDHD2 pathway. Overall, circRUNX1 plays an oncogenic role in PTC and provides a potentially effective therapeutic strategy for PTC progression.

A miRNA-clinicopathological nomogram for the prediction of central lymph node metastasis in papillary thyroid carcinoma-analysis from TCGA database

It is of significance to evaluate central lymph node status in patients with papillary thyroid carcinoma (PTC), because it can decrease postoperative complications resulting from unnecessary prophylactic central lymph node dissection (CLND). Due to the low sensitivity and specificity of neck ultrasonography in the evaluation of central lymph node metastasis (CLNM), it is urgently required to find alternative biomarkers to predict CLNM in PTC patients, which is the main purpose of this study.RNA-sequencing datasets and clinical data of 506 patients with thyroid carcinoma from the Cancer Genome Atlas (TCGA) database were downloaded and analyzed to identify differentially expressed miRNAs (DEMs), which can independently predict CLNM in PTC. A nomogram predictive of CLNM was developed based on clinical characteristics and the identified miRNAs. Receiver operating characteristics curves were drawn to evaluate the predictive performance of the nomogram. Bioinformatics analyses, including target genes identification, functional enrichment analysis, and protein-protein interaction network, were performed to explore the potential roles of the identified DEMs related to CLNM in PTC.A total of 316 PTC patients were included to identify DEMs. Two hundred thirty-seven (75%) PTC patients were randomly selected from the 316 patients as a training set, while the remaining 79 (25%) patients were regarded as a testing set for validation. Two DEMs, miRNA-146b-3p (HR: 1.327, 95% CI = 1.135-1.551, P = .000) and miRNA-363-3p (HR: 0.714, 95% CI = 0.528-0.966, P = .029), were significantly associated with CLNM. A risk score based on these 2 DEMs and calculating from multivariate logistic regression analysis, was significantly lower in N0 group over N1a group in both training (N0 vs N1a: 2.04 ± 1.01 vs 2.73 ± 0.61, P = .000) and testing (N0 vs N1a: 2.20 ± 0.93 vs 2.79 ± 0.68, P = .003) sets. The nomogram including risk score, age, and extrathyroidal extension (ETE) was constructed in the training set and was then validated in the testing set, which showed better prediction value than the other three predictors (risk score, age, and ETE) in terms of CLNM identification. Bioinformatics analyses revealed that 5 hub genes, SLC6A1, SYT1, COL19A1, RIMS2, and COL1A2, might involve in pathways including extracellular matrix organization, ion transmembrane transporter activity, axon guidance, and ABC transporters.On the basis of this study, the nomogram including risk score, age, and ETE showed good prediction of CLNM in PTC, which has a potential to facilitate individualized decision for surgical plans.

PERP-ing into diverse mechanisms of cancer pathogenesis: Regulation and role of the p53/p63 effector PERP

The tetraspan plasma membrane protein PERP (p53 apoptosis effector related to PMP22) is a lesser-known transcriptional target of p53 and p63. A member of the PMP22/GAS3/EMP membrane protein family, PERP was originally identified as a p53 target specifically trans-activated during apoptosis, but not during cell-cycle arrest. Several studies have since shown downregulation of PERP expression in numerous cancers, suggesting that PERP is a tumour suppressor protein. This review focusses on the important advances made in elucidating the mechanisms regulating PERP expression and its function as a tumour suppressor in diverse human cancers, including breast cancer and squamous cell carcinoma. Investigating PERP's role in clinically-aggressive uveal melanoma has revealed that PERP engages a positive-feedback loop with p53 to regulate its own expression, and that p63 is required beside p53 to achieve pro-apoptotic levels of PERP in this cancer. Furthermore, the recent discovery of the apoptosis-mediating interaction of PERP with SERCA2b at the plasma membrane-endoplasmic reticulum interface demonstrates a novel mechanism of PERP stabilisation, and how PERP can mediate Ca²⁺ signalling to facilitate apoptosis. The multi-faceted role of PERP in cancer, involving well-documented functions in mediating apoptosis and cell-cell adhesion is discussed, alongside PERP's emerging roles in epithelial-mesenchymal transition, and PERP crosstalk with inflammation signalling pathways, and other signalling pathways. The potential for restoring PERP expression as a means of cancer therapy is also considered.

Identification of gene co-expression modules and hub genes associated with the invasiveness of pituitary adenoma

In pituitary adenoma (PA), invasiveness is the main cause of recurrence and poor prognosis. Thus, identifying specific biomarkers for diagnosis and effective treatment of invasive PAs is of great clinical significance. In this study, from the Gene Expression Omnibus database, we obtained and combined several microarrays of PA by the "sva" R package. Weighted gene co-expression network analysis was performed to construct a scale-free topology model and analyze the relationships between the modules and clinical traits. Our analysis results indicated that three key modules (dark turquoise, saddle brown, and steel blue) were associated with the invasiveness of PA. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology analysis were performed for the functional annotation of the key modules. In addition, the hub genes in the three modules were identified and screened by differential expression analysis between normal samples and PA samples. Three upregulated differentially expressed genes (DGAT2, PIGZ, and DHRS2) were identified. The Fisher's exact test and receiver operating characteristic curve were used to validate the capability of these genes to distinguish invasive traits, and transcription factor interaction networks were used to further explore the underlying mechanisms of the three genes. Moreover, a lower expression level of DGAT2 in invasive PA tissue than in noninvasive PA tissue was validated by quantitative reverse transcription-polymerase chain reaction. In general, this study contributes to potential molecular biomarkers of invasive PAs and provides a broader perspective for diagnosis and new therapeutic targets for the invasive PAs.

Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis

BACKGROUND

Papillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC.

METHODS

Based on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC.

RESULTS

Black and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC.

CONCLUSIONS

The hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.

Risk score based on expression of five novel genes predicts survival in soft tissue sarcoma

In this study, The Cancer Genome Atlas and Genotype-Tissue Expression databases were used to identify potential biomarkers of soft tissue sarcoma (STS) and construct a prognostic model. The model was used to calculate risk scores based on the expression of five key genes, among which MYBL2 and FBN2 were upregulated and TSPAN7, GCSH, and DDX39B were downregulated in STS patients. We also examined gene signatures associated with the key genes and evaluated the model’s clinical utility. The key genes were found to be involved in the cell cycle, DNA replication, and various cancer pathways, and gene alterations were associated with a poor prognosis. According to the prognostic model, risk scores negatively correlated with infiltration of six types of immune cells. Furthermore, age, margin status, presence of metastasis, and risk score were independent prognostic factors for STS patients. A nomogram that incorporated the risk score and other independent prognostic factors accurately predicted survival in STS patients. These findings may help to improve prognostic prediction and aid in the identification of effective treatments for STS patients.

Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis

Background

Oral squamous cell carcinoma (OSCC) is a major lethal malignant cancer of the head and neck region, yet its molecular mechanisms of tumourigenesis are still unclear.

Patients and methods

We performed weighted gene co-expression network analysis (WGCNA) on RNA-sequencing data with clinical information obtained from The Cancer Genome Atlas (TCGA) database. The relationship between co-expression modules and clinical traits was investigated by Pearson correlation analysis. Furthermore, the prognostic value and expression level of the hub genes of these modules were validated based on data from the TCGA database and other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database. The significant modules and hub genes were also assessed by functional analysis and gene set enrichment analysis (GSEA).

Results

We found that the turquoise module was strongly correlated with pathologic T stage and significantly enriched in critical functions and pathways related to tumourigenesis. PPP1R12B, CFD, CRYAB, FAM189A2 and ANGPTL1 were identified and statistically validated as hub genes in the turquoise module and were closely implicated in the prognosis of OSCC. GSEA indicated that five hub genes were significantly involved in many well-known cancer-related biological functions and signaling pathways.

Conclusion

In brief, we systematically discovered a co-expressed turquoise module and five hub genes associated with the pathologic T stage for the first time, which provided further insight that WGCNA may reveal the molecular regulatory mechanism involved in the carcinogenesis and progression of OSCC. In addition, the five hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for the precise early diagnosis, clinical treatment and prognosis of OSCC in the future.