An integrated transcriptomic and computational analysis for biomarker identification in human glioma

Gelsolin, an Actin-Binding Protein: Bioinformatic Analysis and Functional Significance in Urothelial Bladder Carcinoma

The involvement of the actin-regulatory protein, gelsolin (GSN), in neoplastic transformation has been reported in different cancers including bladder cancer. However, the exact mechanism by which GSN influences bladder cancer development is not well understood. Here, we sought to reveal the functional significance of GSN in bladder cancer by undertaking a comprehensive bioinformatic analysis of TCGA datasets and through the assessment of multiple biological functions. GSN expression was knocked down in bladder cancer cell lines with two siRNA isoforms targeting GSN. Proliferation, migration, cell cycle and apoptosis assays were carried out. GSN expression, enrichment analysis, protein-protein interaction and immune infiltration analysis were verified through online TCGA tools. The data indicated that GSN expression is associated with bladder cancer proliferation, migration and enhanced cell apoptosis through regulation of NF-κB expression. GSN expression correlated with various inflammatory cells and may influence the immunity of the tumor microenvironment. Computational analysis identified several interacting partners which are associated with cancer progression and patient outcome. The present results demonstrate that GSN plays an important role in bladder cancer pathogenesis and may serve as a potential biomarker and therapeutic target for cancer therapy.

Differential Intracellular Protein Distribution in Cancer and Normal Cells-Beta-Catenin and CapG in Gynecologic Malignancies

Simple Summary

The distribution and mobility of proteins inside the living cell can be used to differentiate cancer from normal cells. This review highlights differential protein distribution of two exemplary proteins, beta-catenin and CapG, and their role in gynecologic cancers. Recognizing differential protein distribution in cancer cells may have diagnostic and therapeutic implications.

Abstract

It is well-established that cancer and normal cells can be differentiated based on the altered sequence and expression of specific proteins. There are only a few examples, however, showing that cancer and normal cells can be differentiated based on the altered distribution of proteins within intracellular compartments. Here, we review available data on shifts in the intracellular distribution of two proteins, the membrane associated beta-catenin and the actin-binding protein CapG. Both proteins show altered distributions in cancer cells compared to normal cells. These changes are noted (i) in steady state and thus can be visualized by immunohistochemistry—beta-catenin shifts from the plasma membrane to the cell nucleus in cancer cells; and (ii) in the dynamic distribution that can only be revealed using the tools of quantitative live cell microscopy—CapG shuttles faster into the cell nucleus of cancer cells. Both proteins may play a role as prognosticators in gynecologic malignancies: beta-catenin in endometrial cancer and CapG in breast and ovarian cancer. Thus, both proteins may serve as examples of altered intracellular protein distribution in cancer and normal cells.

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

Identification of the hub genes and prognostic indicators of gastric cancer and correlation of indicators with tumor-infiltrating immune cell levels

Aims: To identify the hub genes and prognostic indicators of gastric cancer (GC) and determine the correlation between prognostic indicators and the tumor-infiltrating immune cell levels so as to provide useful information for future GC diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset and two microarray datasets were used to screen the overlapping differentially expressed genes (DEGs) between normal gastric and GC tissue samples. Hub genes were screened via protein-protein interaction networks and module analysis of the overlapping DEGs. Their expression was validated at the cell level and tissue level using the ONCOMINE database. The prognostic indicators of overall survival (OS) and disease-free survival was identified by Cox proportional hazards regression analysis based on tumor grade and cancer stage. The expression of hub genes was validated at the cell level. The correlation of prognostic indicators with the tumor-infiltrating immune cell levels was analyzed using Tumor IMmune Estimation Resource. Results: Ten hub genes, namely CDC6, CDC20, BUB1B, TOP2A, CDK1, AURKA, CCNA2, CCNB1, MAD2L1, and KIF11, were screened and their upregulation in the GC tissue was verified. Three prognostic factors, namely LUM, VCAN, and EFNA4, were identified; their expression was higher in GC cells than in normal cells. LUM, VCAN, and EFNA4 were correlated with tumor-infiltrating immune cell levels in GC. Significance: The identified hub genes and prognostic indicators of GC could be useful indicators for future GC diagnosis and treatment.

Cross-Species Proteomics Identifies CAPG and SBP1 as Crucial Invasiveness Biomarkers in Rat and Human Malignant Mesothelioma

Malignant mesothelioma (MM) still represents a devastating disease that is often detected too late, while the current effect of therapies on patient outcomes remains unsatisfactory. Invasiveness biomarkers may contribute to improving early diagnosis, prognosis, and treatment for patients, a task that could benefit from the development of high-throughput proteomics. To limit potential sources of bias when identifying such biomarkers, we conducted cross-species proteomic analyzes on three different MM sources. Data were collected firstly from two human MM cell lines, secondly from rat MM tumors of increasing invasiveness grown in immunocompetent rats and human MM tumors grown in immunodeficient mice, and thirdly from paraffin-embedded sections of patient MM tumors of the epithelioid and sarcomatoid subtypes. Our investigations identified three major invasiveness biomarkers common to the three tumor sources, CAPG, FABP4, and LAMB2, and an additional set of 25 candidate biomarkers shared by rat and patient tumors. Comparing the data to proteomic analyzes of preneoplastic and neoplastic rat mesothelial cell lines revealed the additional role of SBP1 in the carcinogenic process. These observations could provide new opportunities to identify highly vulnerable MM patients with poor survival outcomes, thereby improving the success of current and future therapeutic strategies.

Prognostic and clinicopathological significance of CapG in various cancers: Evidence from a meta-analysis

BACKGROUND

The gelsolin-like actin-capping protein (CapG) is an actin-binding protein in the gelsolin superfamily. Increasing evidence indicates that CapG is highly expressed in various types of cancer. However, the role of CapG in malignant tumors is still controversial. Therefore, we conducted a meta-analysis to assess the prognostic value and clinicopathological significance of CapG in malignant tumors.

METHOD

We searched for eligible studies in the PubMed, Web of Science, Embase, and Cochrane databases. Stata SE12.0 software was used for quantitative meta-analysis. The hazard ratios (HRs) and odds ratios (ORs) with 95% CI were pooled to assess the relationship between CapG expression and overall survival (OS), as well as clinicopathological parameters.

RESULTS

Sixteen studies with a total of 1987 cancer patients were included in this meta-analysis. The results showed that higher CapG expression was statistically correlated with shorter OS (HR 1.70, 95% CI 1.43-1.97, P < 0.001), positive lymph node metastasis (OR 1.91, 95% CI 1.19-3.09, P =  0.008), advanced TNM stage (OR 1.87, 95% CI 1.17-3.00, P = 0.009), advanced T-primary stage (OR 2.54, 95% CI 1.08-6.00, P =  0.033) and male sex (OR 1.77, 95% CI 1.23-2.56, P =  0.002). However, no significant correlation was observed between increased CapG expression and advanced age, larger tumor size, differentiation, or advanced histopathologic grading (P > 0.05).

CONCLUSIONS

High CapG expression is associated with a poor prognosis and worse clinicopathological parameters in various cancers. CapG is a potential prognostic biomarker and a possible clinicopathological predictive factor for various cancers.

Analysis of clinical characteristics of macrophage capping protein (CAPG) gene expressed in glioma based on TCGA data and clinical experiments

Macrophage capping protein (CAPG) genes were investigated based on The Cancer Genome Atlas (TCGA) database and clinical experiments. Glioblastoma (GBM) genes expression profiling chip of 529 disease samples and 10 normal samples selected from TCGA database were used for analysis, 25 brain glioma tissue samples and 15 normal brain tissues were collected in the Department of Neurosurgery of the First Affiliated Hospital of Xinjiang Medical University in China from 2016 to 2017 to analyze CAPG genes. TCGA results showed that the expression level of CAPG genes in GBM was higher than that in normal tissues, and the expression level of men, aged over 46 years and high grade gliomas in pathological stages was higher than that of women, aged ≤46 and low grade gliomas in pathological stages, and the survival time of high expression was shorter than that of low expression. The expression level of CAPG in glioma tissues was higher than that in normal tissues, and the expression level of CAPG in males was higher than that in females, as males had lymphatic transfer and low differentiation compared with females, but the expression level was not related to age. Survival analysis showed that higher expression level indicated shorter survival time, they were positively correlated. The expression of CAPG in glioma is high, and it is highly expressed with the severity of the disease, and it is also obviously related to the prognosis. Therefore, CAPG could be used as a biomarker for pathological grade and prognosis in glioma. However, the related studies are not consistent on the expression of different sex and ages, so further study is needed.

Screening differentially expressed proteins from co-cultured hematopoietic cells and bone marrow-derived stromal cells by quantitative proteomics (SILAC) method

Background

Bone marrow stromal cells protect hematopoietic cells and provide drug resistance by delivering bunch of variable proteins. Thus, alterations of protein expression are typically associated with cell–cell signal transduction and regulation of cellular functions.

Methods

Co-culture models of bone marrow stromal cells and hematopoietic cells are often used in studies of their crosstalk. Studies of altered protein expression initiated by stromal cell/hematopoietic cell interactions are an important new trend in microenvironmental research. There has been no report to date of global quantitative proteomics analysis of crosstalk between hematopoietic cells and stromal cells. In this study, we analyzed quantitative proteomes in a co-culture system of stromal HS5 cells and hematopoietic KG1a cells, and simultaneously tracked differentially expressed proteins in two types of cells before and after co-culture by stable isotope labeling by amino acids in cell culture (SILAC) method.

Results

We have shown that in co-cultured KG1a, 40 proteins (including CKAP4, LMNA, and SERPINB2) were upregulated and 64 proteins (including CD44, CD99, and NCAM1) were downregulated relative to KG1a alone. We utilized IPA analysis to discover that the NOD-like receptor signaling pathway was upregulated, whereas platelet activation was downregulated in co-cultured KG1a cells. Furthermore, 95 proteins (including LCP1, ARHGAP4, and UNCX) were upregulated and 209 proteins (including CAPG, FLNC, and MAP4) were downregulated in co-cultured HS5 relative to HS5 alone. The tight junction pathway was downregulated and glycolysis/gluconeogenesis pathway was dysfunctional in co-cultured HS5. Most importantly, the significantly differentially expressed proteins can also be confirmed using different co-cultured cell lines.

Conclusion

Altogether, we recommend such quantitative proteomics approach for the studies of the hematopoietic–stroma cross-talk, differentially expressed proteins and related signaling pathways identification. The differentially expressed proteins identified from this current SILAC method will provide a useful basis for ongoing studies of crosstalk between stromal cells and hematopoietic cells in co-culture systems. All these result suggested our ongoing studies can focus on the mechanisms underlying CKAP4 increase and CD44 decrease in co-cultured hematopoietic cells, and the increase of LCP1 and decrease of CAPG in co-cultured stromal cell. The proteomic profiles from the KG1a/stromal cell co-culture system give new molecular insights into the roles of these cells in MDS pathophysiology and related bone disease.

Electronic supplementary material

The online version of this article (10.1186/s12014-019-9249-x) contains supplementary material, which is available to authorized users.

Capping Actin Protein Overexpression in Human Colorectal Carcinoma and Its Contributed Tumor Migration

Objective

Human colorectal cancer (CRC) is the third most common cancer; patients with metastatic colorectal cancer (mCRC) show poor prognosis than those with CRC cases. There are no reliable molecular biomarkers for the diagnosis of CRC prognosis except with pathological features. Therefore, it is urgent to develop a biomarker for diagnosis and/or prediction of human CRC. In addition, capping actin protein (CapG) belongs to the gelsolin family and has been reported to contribute on tumor invasion/metastasis in multiple human cancers. Here, we are the first to evaluate the expression of CapG in human CRCs.

Study Design

To investigate the expression levels of CapG in human tissue array by immunohistochemistry (IHC) staining. Moreover, the mRNA and protein levels were also confirmed in four CRC cell lines and determined using real-time RT-PCR and Western blotting. Finally, a Matrigel transwell invasion assay was used to evaluate the invasion ability in CapG high or low expression cells.

Results

We demonstrated that CapG could be determined in the normal colon tissue and human CRC specimens. However, CapG was significantly overexpressed in the mCRC specimens compared with that in CRC specimens and normal cases. It was also detectable in the four CRC cell lines including mRNA and protein levels. We also found that knockdown of the expression of CapG reduced tumor migration.

Conclusions

In this study, we suggested that CapG could be used as a biomarker for metastatic CRC in the clinical specimens. Moreover, our in vitro study demonstrated that CapG might contribute on tumor metastasis in human CRCs.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.