Identification of a Six Gene Prognosis Signature for Papillary Thyroid Cancer Using Multi-Omics Methods and Bioinformatics Analysis

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.

Integrated analysis of tumor microenvironment features to establish a diagnostic model for papillary thyroid cancer using bulk and single-cell RNA sequencing technology

BACKGROUND

Characterizing tumor microenvironment using single-cell RNA sequencing has been a promising strategy for cancer diagnosis and treatment. However, a few studies have focused on diagnosing papillary thyroid cancer (PTC) through this technology. Therefore, our study explored tumor microenvironment (TME) features and identified potential biomarkers to establish a diagnostic model for papillary thyroid cancer.

METHODS

The cell types were identified using the markers from the CellMarker database and published research. The CellChat package was conducted to analyze the cell-cell interaction. The SCEVAN package was used to identify malignant thyroid cells. The SCP package was used to perform multiple single-cell downstream analyses, such as GSEA analysis, enrichment analysis, pseudotime trajectory analysis, and differential expression analysis. The diagnostic model of PTC was estimated using the calibration curves, receiver operating characteristic curves, and decision curve analysis. RT-qPCR was performed to validate the expression of candidate genes in human papillary thyroid samples.

RESULTS

Eight cell types were identified in the scRNA-seq dataset by published cell markers. Extensive cell-cell interactions like FN1/ITGB1 existed in PTC tissues. We identified 26 critical genes related to PTC progression. Further, eight subgroups of PTC tumor cells were identified and exhibited high heterogeneity. The MDK/LRP1, MDK/ALK, GAS6/MERTK, and GAS6/AXL were identified as potential ligand-receptor pairs involved in the interactions between fibroblasts/endothelial cells and tumor cells. Eventually, the diagnostic model constructed by TRPC5, TENM1, NELL2, DMD, SLC35F3, and AUTS2 showed a good efficiency for distinguishing the PTC and normal tissues.

CONCLUSIONS

Our study comprehensively characterized the tumor microenvironment in papillary thyroid cancer. Through combined analysis with bulk RNA-seq, six potential diagnostic biomarkers were identified and validated. The diagnostic model we constructed was a promising tool for PTC diagnosis. Our findings provide new insights into the heterogeneity of thyroid cancer and the theoretical basis for diagnosing thyroid cancer.

An Immune-Related Gene Signature for Predicting Survival and Immunotherapy Efficacy in Esophageal Adenocarcinoma

BACKGROUND Immune checkpoint inhibitor (ICI) therapy has attracted wide attention in the treatment of malignant tumors. This study was designed to build a prognostic model based on immune-related genes for esophageal adenocarcinoma (EAC). MATERIAL AND METHODS The expression of immune-related differentially-expressed genes (IRDEGs) between EAC and normal samples from The Cancer Genome Atlas database was analyzed. Univariate and multivariate Cox regressions were used to identify the prognostic IRDEGs and construct an immune-related gene signature (IRGS) to predict the overall survival (OS) of EAC patients. Then, the molecular mechanisms and immune characteristics were comprehensively analyzed. RESULTS A total of 111 IRDEGs were obtained from the weighted gene co-expression network analysis. Univariate Cox regression analysis showed that 12 IRDEGs (P<0.05 for all) were linked with OS in the EAC patients. Four genes were used to construct the IRGS based on the multivariate Cox regression analysis. Patients in the high-risk group showed worse OS than those in the low-risk group (P<0.001). A high-risk score was related to DNA replication relevant pathways, an increase in mutation rate, and an increase in activated mast cell infiltration. Patients with high-risk scores had lower tumor immune dysfunction and exclusion scores (P<0.001). CONCLUSIONS IRDEGs may be involved in the progression of EAC. The high-risk group is more suitable for immunotherapy, which may provide a reference value for the treatment of clinical EAC patients. Therefore, it is possible to identify the patients who are better suited for ICI therapy.

Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis

Objective

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms.

Methods

Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined.

Results

A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils.

Conclusion

COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.

A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma

Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.

An integrative analysis of the tumor suppressors and oncogenes from sexual dimorphism and gene expression alteration features in thyroid cancer

BACKGROUND

The incidence of thyroid cancer has risen rapidly over the last decades. Although mortality rates are relatively low compared to other cancers, the rate of new cases started to increase in the early 2000s. While tumor suppressors and oncogenes were recently identified in thyroid cancer, the potential roles of these genes in thyroid cancer remain unclear.

OBJECTIVE

Analyze the roles and functions of tumor suppressors and oncogenes in thyroid cancer.

METHODS

Thyroid cancer data were collected from public databases, such as the UCSC Xena database of TCGA thyroid cancer, TISIDB, and UALCAN. The genes frequently associated with unfavorable thyroid cancer were examined and validated. The association of these target genes with thyroid tumorigenesis, stages, subtypes, and survival rates were analyzed. Additionally, the genes aberrantly expressed in thyroid cancer and significantly involved in thyroid tumorigenesis, stages, subtypes, and survival rates were identified.

RESULTS

Female sex was identified as a risk factor for thyroid cancer. The expression of PAPSS2, PDLIM3, COPZ2, ALDH1B1, ANTXR1, GUF1, and SENP6 negatively correlated with thyroid cancer prognosis.

CONCLUSION

Female sex was a risk factor for thyroid cancer. In addition, our analysis suggested that PAPSS2, PDLIM3, COPZ2, ALDH1B1, ANTXR1, GUF1, and SENP6 are negatively correlated with the prognosis of thyroid cancer. The expression of ANTXR1, GUF1, and PDLIM3 was weakly associated with thyroid cancer's immune and molecular subtypes.

Osteoglycin: An ECM Factor Regulating Fibrosis and Tumorigenesis

The extracellular matrix (ECM) is made up of noncellular components that have special properties for influencing cell behavior and tissue structure. Small leucine-rich proteoglycans (SLRPs) are nonfibrillar ECM components that serve as structural scaffolds and signaling molecules. osteoglycin (OGN), a class III SLRP, is a ubiquitous ECM component that not only helps to organize the extracellular matrix but also regulates a number of important biological processes. As a glycosylated protein in the ECM, OGN was originally considered to be involved in fiber assembly and was reported to have a connection with fibrosis. In addition to these functions, OGN is found in a variety of cancer tissues and is implicated in cellular processes linked to tumorigenesis, including cell proliferation, invasion, metastasis, and epithelial-mesenchymal transition (EMT). In this review, we summarize the structure and functions of OGN as well as its biological and clinical importance in the context of fibrotic illness and tumorigenesis. This review aims to improve our understanding of OGN and provide some new strategies for the treatment of fibrosis and cancer.

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer

BACKGROUND

The present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.

METHODS

Differentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.

RESULTS

We constructed a prognostic predictive risk model of prostate cancer containing ten immune-related RBP genes. We found that high-risk prostate cancer patients presented poorer prognosis, higher tumor immune cell infiltration, higher rates of genomic alterations, and were more sensitive to targeted and immunotherapy than the low-risk group.

CONCLUSIONS

The immune-related RBPs' signature is an independent prognostic marker that could help screen patients with advanced prostate cancer who are better suited for targeted and immunotherapy.

P-Cadherin Regulates Intestinal Epithelial Cell Migration and Mucosal Repair, but Is Dispensable for Colitis Associated Colon Cancer

Recurrent chronic mucosal inflammation, a characteristic of inflammatory bowel diseases (IBD), perturbs the intestinal epithelial homeostasis resulting in formation of mucosal wounds and, in most severe cases, leads to colitis-associated colon cancer (CAC). The altered structure of epithelial cell-cell adhesions is a hallmark of intestinal inflammation contributing to epithelial injury, repair, and tumorigenesis. P-cadherin is an important adhesion protein, poorly expressed in normal intestinal epithelial cells (IEC) but upregulated in inflamed and injured mucosa. The goal of this study was to investigate the roles of P-cadherin in regulating intestinal inflammation and CAC. P-cadherin expression was markedly induced in the colonic epithelium of human IBD patients and CAC tissues. The roles of P-cadherin were investigated in P-cadherin null mice using dextran sulfate sodium (DSS)-induced colitis and an azoxymethane (AOM)/DSS induced CAC. Although P-cadherin knockout did not affect the severity of acute DSS colitis, P-cadherin null mice exhibited faster recovery after colitis. No significant differences in the number of colonic tumors were observed in P-cadherin null and control mice. Consistently, the CRISPR/Cas9-mediated knockout of P-cadherin in human IEC accelerated epithelial wound healing without affecting cell proliferation. The accelerated migration of P-cadherin depleted IEC was driven by activation of Src kinases, Rac1 GTPase and myosin II motors and was accompanied by transcriptional reprogramming of the cells. Our findings highlight P-cadherin as a negative regulator of IEC motility in vitro and mucosal repair in vivo. In contrast, this protein is dispensable for IEC proliferation and CAC development.

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 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.

Comprehensive Pan-Cancer Analysis and the Regulatory Mechanism of ASF1B, a Gene Associated With Thyroid Cancer Prognosis in the Tumor Micro-Environment

Background

The incidence of thyroid cancer, whose local recurrence and metastasis lead to death, has always been high and the pathogenesis of papillary thyroid carcinoma (PTC) has not been clearly elucidated. Therefore, the research for more accurate prognosis-related predictive biomarkers is imminent, and a key gene can often be a prognostic marker for multiple tumors.

Methods

Gene expression profiles of various cancers in the TCGA and GTEx databases were downloaded, and genes significantly associated with the prognosis of THCA were identified by combining differential analysis with survival analysis. Then, a series of bioinformatics tools and methods were used to analyze the expression of the gene in each cancer and the correlation of each expression with prognosis, tumor immune microenvironment, immune neoantigens, immune checkpoints, DNA repair genes, and methyltransferases respectively. The possible biological mechanisms were also investigated by GSEA enrichment analysis.

Results

656 differentially expressed genes were identified from two datasets and 960 DEGs that were associated with disease-free survival in THCA patients were screened via survival analysis. The former and the latter were crossed to obtain 7 key genes, and the gene with the highest risk factor, ASF1B, was selected for this study. Differential analysis of multiple databases showed that ASF1B was commonly and highly expressed in pan-cancer. Survival analysis showed that high ASF1B expression was significantly associated with poor patient prognosis in multiple cancers. In addition, ASF1B expression levels were found to be associated with tumor immune infiltration in THCA, KIRC, LGG, and LIHC, and with tumor microenvironment in BRCA, LUSC, STAD, UCEC, and KIRC. Further analysis of the relationship between ASF1B expression and immune checker gene expression suggested that ASF1B may regulate tumor immune patterns in most tumors by regulating the expression levels of specific immune checker genes. Finally, GSEA enrichment analysis showed that ASF1B high expression was mainly enriched in cell cycle, MTORC1 signaling system, E2F targets, and G2M checkpoints pathways.

Conclusions

ASF1B may be an independent prognostic marker for predicting the prognosis of THCA patients. The pan-cancer analysis suggested that ASF1B may play an important role in the tumor micro-environment and tumor immunity and it has the potential of serving as a predictive biomarker for multiple cancers.