Clinical significance of CCBE1 expression in lung cancer

Identification of the hub genes associated with prostate cancer tumorigenesis

Introduction

Prostate cancer (PCa) is one of the most common malignant tumors of the male urogenital system; however, the underlying mechanisms remain largely unclear. This study integrated two cohort profile datasets to elucidate the potential hub genes and mechanisms in PCa.

Methods and Results

Gene expression profiles GSE55945 and GSE6919 were filtered from the Gene Expression Omnibus (GEO) database to obtain 134 differentially expressed genes (DEGs) (14 upregulated and 120 downregulated) in PCa. Gene Ontology and pathway enrichment were performed using the Database for Annotation, Visualization, and Integrated Discovery, showing that these DEGs were mainly involved in biological functions such as cell adhesion, extracellular matrix, migration, focal adhesion, and vascular smooth muscle contraction. The STRING database and Cytoscape tools were used to analyze protein-protein interactions and identify 15 hub candidate genes. Violin plot, boxplot, and prognostic curve analyses were performed using Gene Expression Profiling Interactive Analysis, which identified seven hub genes, including upregulated expressed SPP1 and downregulated expressed MYLK, MYL9, MYH11, CALD1, ACTA2, and CNN1 in PCa compared with normal tissue. Correlation analysis was performed using the OmicStudio tools, which showed that these hub genes were moderately to strongly correlated with each other. Finally, quantitative reverse transcription PCR and western blotting were performed to validate the hub genes, showing that the abnormal expression of the seven hub genes in PCa was consistent with the analysis results of the GEO database.

Discussion

Taken together, MYLK, MYL9, MYH11, CALD1, ACTA2, SPP1, and CNN1 are hub genes significantly associated with PCa occurrence. These genes are abnormally expressed, leading to the formation, proliferation, invasion, and migration of PCa cells and promoting tumor neovascularization. These genes may serve as potential biomarkers and therapeutic targets in patients with PCa.

Proteomic Analysis Reveals Key Proteins in Extracellular Vesicles Cargo Associated with Idiopathic Pulmonary Fibrosis In Vitro

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, irreversible, and highly fatal disease. It is characterized by the increased activation of both fibroblast and myofibroblast that results in excessive extracellular matrix (ECM) deposition. Extracellular vesicles (EVs) have been described as key mediators of intercellular communication in various pathologies. However, the role of EVs in the development of IPF remains poorly understood. This study aimed to characterize the differentially expressed proteins contained within EVs cargo derived from the fibroblast cell lines LL97A (IPF-1) and LL29 (IPF-2) isolated from lungs bearing IPF as compared to those derived from the fibroblast cell lines CCD8Lu (NL-1) and CCD19Lu (NL-2) isolated from healthy donors. Isolated EVs were subjected to label-free quantitative proteomic analysis by LC-MS/MS, and as a result, 331 proteins were identified. Differentially expressed proteins were obtained after the pairwise comparison, including all experimental groups. A total of 86 differentially expressed proteins were identified in either one or more comparison groups. Of note, proteins involved in fibrogenic processes, such as tenascin-c (TNC), insulin-like-growth-factor-binding protein 7 (IGFBP7), fibrillin-1 (FBN1), alpha-2 collagen chain (I) (COL1A2), alpha-1 collagen chain (I) (COL1A1), and lysyl oxidase homolog 1 (LOXL1), were identified in EVs cargo isolated from IPF cell lines. Additionally, KEGG pathway enrichment analysis revealed that differentially expressed proteins participate in focal adhesion, PI3K-Akt, and ECM–receptor interaction signaling pathways. In conclusion, our findings reveal that proteins contained within EVs cargo might play key roles during IPF pathogenesis.

Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

Background

Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.

Methods

Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.

Results

GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.

Conclusion

In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

miR-942-5p Inhibits Proliferation, Metastasis, and Epithelial-Mesenchymal Transition in Colorectal Cancer by Targeting CCBE1

Although colorectal cancer (CRC) is common, there is a paucity of information regarding its molecular pathogenesis. Studies have shown that miRNAs play pivotal roles in the development and progression of CRC. There is a need to further investigate the biological functions of miRNAs in CRC. In particular, it has been reported that miR-942-5p exhibits tumor-suppressive properties. Thus, we analyzed the functional significance of miR-942-5p in CRC and the underlying molecular mechanisms. We found that miR-942-5p was downregulated in CRC tissues and cells. Cell Counting Kit-8, EdU, and colony formation assays revealed that the overexpression of miR-942-5p by mimics inhibited the proliferation of CRC cells. Use of the miR-942-5p inhibitor effectively enhanced the proliferative potential of CRC cells. Further, in vivo xenograft experiments confirmed these results. Increased expression of miR-942-5p suppressed the invasion, migration, and epithelial-mesenchymal transition of CRC cell lines, while decreased miR-942-5p expression had the opposite effect. CCBE1, a secretory molecule for lymphangiogenesis, was established as a downstream target of miR-942-5p, and its expression was inversely correlated with the expression of miR-942-5p in CRC cells. Additionally, cotransfection of the miR-942-5p inhibitor with si-CCBE1 into CRC cells reversed the effects induced by miR-942-5p overexpression. In conclusion, we confirmed that miR-942-5p exerts oncogenic actions in CRC by targeting CCBE1 and identified miR-942-5p as a potential clinical biomarker for CRC diagnosis and therapy.

Improved understanding of gastrointestinal stromal tumors biology as a step for developing new diagnostic and therapeutic schemes

A gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the human gastrointestinal tract, with an estimated incidence of 10-15 per 1 million per year. While preparing holistic care for patients with GIST diagnosis, scientists might face several difficulties - insufficient risk stratification, acquired or secondary resistance to imatinib, or the need for an exceptional therapy method associated with wild-type tumors. This review summarizes recent advances associated with GIST biology that might enhance diagnostic and therapeutic strategies. New molecules might be incorporated into risk stratification schemes due to their proven association with outcomes; however, further research is required. Therapies based on the significant role of angiogenesis, immunology, and neural origin in the GIST biology could become a valuable enhancement of currently implemented treatment schemes. Generating miRNA networks that would predict miRNA regulatory functions is a promising approach that might help in better selection of potential biomarkers and therapeutical targets in cancer, including GISTs.

A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

Objective

To develop and validate a novel RNA-seq-based nomogram for preoperative prediction of lymph node metastasis (LNM) for patients with oral squamous cell carcinoma (OSCC).

Methods

RNA-seq data for 276 OSCC patients (including 157 samples with LNM and 119 without LNM) were downloaded from TCGA database. Differential expression analysis was performed between LNM and non-LNM of OSCC. These samples were divided into a training set and a test set by a ratio of 9 : 1 while the relative proportion of the non-LNM and LNM groups was kept balanced within each dataset. Based on clinical features and seven candidate RNAs, we established a prediction model of LNM for OSCC using logistic regression analysis. Tenfold crossvalidation was utilized to examine the accuracy of the nomogram. Decision curve analysis was performed to evaluate the clinical utility of the nomogram.

Results

A total of 139 differentially expressed RNAs were identified between LNM and non-LNM of OSCC. Seven candidate RNAs were screened based on FPKM values, including NEURL1, AL162581.1 (miscRNA), AP002336.2 (lncRNA), CCBE1, CORO6, RDH12, and AC129492.6 (pseudogene). Logistic regression analysis revealed that the clinical N stage (p < 0.001) was an important factor to predict LNM. Moreover, three RNAs including RDH12 (p value < 0.05), CCBE1 (p value < 0.01), and AL162581.1 (p value < 0.05) could be predictive biomarkers for LNM in OSCC patients. The average accuracy rate of the model was 0.7661, indicating a good performance of the model.

Conclusion

Our findings constructed an RNA-seq-based nomogram combined with clinicopathology, which could potentially provide clinicians with a useful tool for preoperative prediction of LNM and be tailored for individualized therapy in patients with OSCC.

Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma

Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan-Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset. Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI. Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

The clinical significance of CCBE1 expression in human colorectal cancer

Purpose

The identification and discovery of prognostic markers for colorectal cancer (CRC) are of great clinical significance. CCBE1 is expressed in various tumors and its expression correlates with lymphangiogenesis and angiogenesis. However, the association between CCBE1 expression and CRC outcome has not been reported. The aim of this study was to investigate clinical significance of CCBE1 expression in CRC.

Patients and methods

CCBE1 expression was examined in 30 pairs of fresh CRC tissues and compared with adjacent normal (AN) tissues using quantitative real-time PCR (qRT-PCR), Western blotting and immunohistochemistry (IHC) staining. Tissue microarray immunohistochemical staining was used to study the CCBE1 expression characteristics of 204 CRC patient samples collected from January 2002 to December 2007, and the relationship of CCBE1 with clinicopathological features and prognosis of CRC was analyzed.

Results

CCBE1 was highly expressed in CRC tissues compared with matched AN tissues (P=0.001). Moreover, high expression of CCBE1 was significantly associated with tumor differentiation, lymph node metastasis, vascular invasion, liver metastasis and TNM stage in CRC patients (P≤0.01). Kaplan-Meier survival analysis revealed that high CCBE1 expression, poor tumor differentiation, lymph node metastasis and vascular invasion were significantly associated (all P<0.001) with poor prognosis for patients. Furthermore, univariate and multivariate Cox analysis revealed that high CCBE1 expression, poor tumor differentiation, lymph node metastasis and vascular invasion were independent risk factors for both overall survival (OS) and disease-free survival (DFS) of CRC patients (all P<0.05). OS and DFS of 267 CRC patients from The Cancer Genome Atlas (TCGA) database showed the same trend (log-rank P=6e-04, HR [high] =2.4; log-rank P=0.0081, HR [high] =1.9).

Conclusion

High levels of CCBE1 contribute to the aggressiveness and poor prognosis of CRC. CCBE1 can serve as a novel potential biomarker to predict CRC patients' prognosis.

Clinical significance of calcium-binding protein S100A8 and S100A9 expression in non-small cell lung cancer

Background

The purpose of this study was to evaluate the correlation between calcium‐binding protein S100A8 and S100A9 expression in non‐small cell lung cancer (NSCLC) and patients’ clinical features.

Methods

Fifty‐two NSCLC patients who underwent surgery at Zhejiang Hospital from February 2014 to January 2016 were included in this study. Calcium‐binding protein S100A8 and S100A9 expression patterns in cancer and para‐cancer tissues were examined by immunohistochemistry assay. The correlation between calcium‐binding protein S100A8 and S100A9 expression patterns and NSCLC patients’ clinical characteristics, including age, gender, tumor node metastasis stage, and pathology type, were evaluated.

Results

S100A8 and S100A9 were generally expressed on the cytoplasm and nucleus of NSCLC cells, mainly located in the cytoplasm, stained with brown particles, and distributed evenly. The positive expression rates of S100A8 and S100A9 in cancer tissues were 71.2% and 76.9%, respectively, which were significantly higher than in para‐cancer tissues at 11.5% and 19.2%, respectively, with statistical significance (P < 0.05). S100A8 and S100A9 positive expression was associated with tumor differentiation degree (P < 0.05) but were not correlated with age, gender, smoking history, tumor diameter, pathology type, tumor node metastasis stage, or pleural effusion (P_all > 0.05).

Conclusion

S100A8 and S100A9 positive expression in cancer tissues was significantly higher than in para‐cancer tissues and was correlated with tumor differentiation, which may be a potential marker for poor prognosis.