Overexpression of collagen VI α3 in gastric cancer

The Prognostic Significance of Collagen VI in Pancreatic Ductal Adenocarcinoma

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and lack of biomarkers. A rich desmoplastic tumor stroma is considered a hallmark of PDAC and previous studies have indicated upregulated expression of collagen VI (COL6) in PDAC. COL6 is shown to associate with prognosis in many cancers but has been less extensively studied in PDAC.

MATERIALS AND METHODS

The expression of COL6 was analyzed by immunohistochemistry in tissue microarrays containing resected tumor tissue samples from PDAC patients (n = 164). Significance of COL6 was estimated with Kaplan-Meier survival estimates and multivariable Cox regression analysis. COL6 protein and mRNA expression patterns were further investigated in publicly available datasets.

RESULTS

There were no statistically significant ( P < 0.05) differences in survival when comparing high and low protein expression of any of the analyzed COL6 α-chains (α1(VI): hazard ratio [HR] 0.90, 95% confidence interval [CI] 0.64-1.28; α2(VI): HR 1.28, 95% CI 0.86-1.89; α3(VI): HR 0.91, 95% CI 0.64-1.29). Similar results were obtained when assessing public data from the Cancer Proteome Atlas, Clinical Proteomic Tumor Analysis Consortium, and The Cancer Genome Atlas.

CONCLUSIONS

In contrast with previous studies and some other cancers, we did not find any association of COL6 tissue expression and PDAC survival.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

Constructing interactive networks of functional genes and metabolites to uncover the cellular events related to colorectal cancer cell migration induced by arsenite

Tumor cell migration induced by arsenite (iAs^III) is closely associated with cancer progression. However, transcriptomic and metabolic traits of migrative human cells exposed to iAs^III remain to be well characterized. Here, the combination of transcriptomics and metabolomics approaches were employed to construct interactive networks of functional genes and metabolites in human colorectal cancer (DLD-1) cells exposed to iAs^III. The number of DLD-1 cells passing through the Transwell membrane was at least 6 times greater in the iAs^III-treated groups than in controls. Following iAs^III treatment, the expression of ZEB1 and SLUG protein was significantly upregulated while the expression of CRB2 was downregulated (p < 0.05), indicating the onset of epithelial to mesenchymal transition (EMT). Meanwhile, integrin- and collagen-mediated biological adhesion were enhanced by SLUG under iAs^III treatment. The expression of matrix metallopeptidase (MMP) genes was fostered by iAs^III, which have the functions to degrade extracellular matrix. Glutamine metabolism could be considerably interfered by iAs^III, and in turn glutamine supplementation could effectively enhance DLD-1 cell movement. Overall, our results suggested that DLD-1 cell migration could be promoted by iAs^III via a series of cellular events, including EMT activation, altered cell adhesion, MMP-dependent matrix degradation, accompanying with a metabolic focus on glutamine.

Screening Protein Prognostic Biomarkers for Stomach Adenocarcinoma Based on The Cancer Proteome Atlas

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment.

Targeting Tumor-Stromal Interactions in Pancreatic Cancer: Impact of Collagens and Mechanical Traits

Pancreatic ductal adenocarcinoma (PDAC) has one of the worst outcomes among cancers with a 5-years survival rate of below 10%. This is a result of late diagnosis and the lack of effective treatments. The tumor is characterized by a highly fibrotic stroma containing distinct cellular components, embedded within an extracellular matrix (ECM). This ECM-abundant tumor microenvironment (TME) in PDAC plays a pivotal role in tumor progression and resistance to treatment. Cancer-associated fibroblasts (CAFs), being a dominant cell type of the stroma, are in fact functionally heterogeneous populations of cells within the TME. Certain subtypes of CAFs are the main producer of the ECM components of the stroma, with the most abundant one being the collagen family of proteins. Collagens are large macromolecules that upon deposition into the ECM form supramolecular fibrillar structures which provide a mechanical framework to the TME. They not only bring structure to the tissue by being the main structural proteins but also contain binding domains that interact with surface receptors on the cancer cells. These interactions can induce various responses in the cancer cells and activate signaling pathways leading to epithelial-to-mesenchymal transition (EMT) and ultimately metastasis. In addition, collagens are one of the main contributors to building up mechanical forces in the tumor. These forces influence the signaling pathways that are involved in cell motility and tumor progression and affect tumor microstructure and tissue stiffness by exerting solid stress and interstitial fluid pressure on the cells. Taken together, the TME is subjected to various types of mechanical forces and interactions that affect tumor progression, metastasis, and drug response. In this review article, we aim to summarize and contextualize the recent knowledge of components of the PDAC stroma, especially the role of different collagens and mechanical traits on tumor progression. We furthermore discuss different experimental models available for studying tumor-stromal interactions and finally discuss potential therapeutic targets within the stroma.

Comprehensive Analysis of IGFBPs as Biomarkers in Gastric Cancer

OBJECTIVE

Gastric cancer is the fifth most common cancer worldwide and the third leading cause of cancer-related deaths. Insulin-like growth-factor-binding proteins (IGFBPs) were initially identified as passive inhibitors that combined with insulin-like growth factors (IGFs) in serum. However, more recent data have shown that they have different expression patterns and a variety of functions in the development and occurrence of cancers. Thus, their various roles in cancer still need to be elucidated. This study aimed to explore the IGFBPs and their prognostic value as markers in gastric cancer.

METHODS

Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, cBioPortal, GeneMANIA, and TIMER were used to analyze the differential expression, prognostic value, genetic alteration, and association with immune cell infiltration of IGFPBs in gastric cancer.

RESULTS

Expression levels of IGFBP3, IGFBP4, and IGFBP7 were significantly elevated in gastric cancer tissues, whereas those of IGFBP1 were reduced in normal tissues. IGFBP1/5/7 expression was significantly associated with overall survival whereas IGFBP6/7 expression was significantly correlated with disease-free survival in gastric cancer patients. IGFBP3/5/6/7 were associated with clinical cancer stage. Gene ontology and Kyoto Encyclopedia of Genes and Genome analyses showed that IGFBP3/5/7 were mainly enriched in focal adhesion, extracellular matrix structural constituent, cell-substratist junction, extracellular structure, and matrix organization. Stomach adenocarcinoma (STAD) and gastric cancer had more IGFBP1-7 mutations than other tumor types. Hub gene analysis showed that TP53 and IGF2 expression was significantly elevated in STAD patients; PLG, PAPPA, AFP, and CYR61 were associated with overall survival rate; and IGFALS, PLG, IGF1, AHSG, and FN1 were associated with disease-free survival. Finally, IGFBP3-7 were all associated with cancer-associated fibroblast infiltration in STAD, colon adenocarcinoma, and rectal adenocarcinoma.

CONCLUSION

Our study provides a comprehensive analysis and selection of IGFBPs as prognostic biomarkers in STAD. This was the first bioinformatic analysis study to describe the involvement of IGFBPs, especially IGFBP7, in gastric cancer development through the extracellular matrix.

Prognostic Value of Highly Expressed Type VII Collagen (COL7A1) in Patients With Gastric Cancer

Collagen is a major component in the tumor microenvironment. This study reveals a novel biomarker candidate, type VII collagen (COL7A1), in patients with gastric cancer. To identify genes differentially expressed in gastric cancer tissue, we analyzed cancerous (n = 20) and noncancerous tissues (n = 13) using a DNA microarray. To perform immunohistochemistry and validate the upregulation of COL7A1 expression, we collected 200 more gastric cancer tissues and 100 normal gastric tissues from 200 randomly selected patients who underwent gastrectomy for gastric cancer between January 2010 and December 2013. The correlations between COL7A1 expression and clinicopathological parameters and patients’ overall survival (OS) were analyzed. In the microarray, COL7A1 was upregulated in gastric cancer tissue compared with normal tissue. In the immunohistochemistry study, COL7A1 was more highly expressed in cancer tissue than in normal tissue (p = 0.001). Patients with intracellular COL7A1 expression had significantly poorer five-year OS than those with only extracellular expression (41.5 versus 69.7%, p = 0.001), and the site of expression was an independent prognostic factor of OS (hazard ratio 2.00, 95% CI 1.26–3.16, p = 0.003). Also, we found a significant association between the COL7A1 immunohistochemistry score and distant metastasis (high versus low, odds ratio 4.45, 95% CI 1.40–14.16, p = 0.011). The site and total immunohistochemistry score of COL7A1 expression in gastric cancer showed prognostic significance for OS and distant metastasis, respectively. COL7A1 could be a novel biomarker with diagnostic and therapeutic value.

Identification of Differentially Expressed Genes in Cervical Cancer Patients by Comparative Transcriptome Analysis

Cervical cancer is one of the most malignant reproductive diseases seen in women worldwide. The identification of dysregulated genes in clinical samples of cervical cancer may pave the way for development of better prognostic markers and therapeutic targets. To identify the dysregulated genes (DEGs), we have retrospectively collected 10 biopsies, seven from cervical cancer patients and three from normal subjects who underwent a hysterectomy. Total RNA isolated from biopsies was subjected to microarray analysis using the human Clariom D Affymetrix platform. Based on the results of principal component analysis (PCA), only eight samples are qualified for further studies; GO and KEGG were used to identify the key genes and were compared with TCGA and GEO datasets. Identified genes were further validated by quantitative real-time PCR and receiver operating characteristic (ROC) curves, and the highest Youden index was calculated in order to evaluate cutoff points (COPs) that allowed distinguishing of tissue samples of cervical squamous carcinoma patients from those of healthy individuals. By comparative microarray analysis, a total of 108 genes common across the six patients' samples were chosen; among these, 78 genes were upregulated and 26 genes were downregulated. The key genes identified were SPP1, LYN, ARRB2, COL6A3, FOXM1, CCL21, TTK, and MELK. Based on their relative expression, the genes were ordered as follows: TTK > ARRB2 > SPP1 > FOXM1 > LYN > MELK > CCL21 > COL6A3; this generated data is in sync with the TCGA datasets, except for ARRB2. Protein-protein interaction network analysis revealed that TTK and MELK are closely associated with SMC4, AURKA, PLK4, and KIF18A. The candidate genes SPP1, FOXM1, LYN, COL6A3, CCL21, TTK and MELK at mRNA level, emerge as promising candidate markers for cervical cancer prognosis and also emerge as potential therapeutic drug targets.

Identifying the tumor-progressive gene expression profile in high-risk papillary thyroid cancer

PURPOSE

Papillary thyroid cancer (PTC) is generally associated with a favorable prognosis. However, some patients have fatal disease, with locally infiltrating tumors or progressive distant metastases; yet few studies have investigated the characteristics of the tumor-progressive gene expression profile in advanced PTC. We conducted this study to clarify the gene expression status in advanced PTC and identify candidate molecules for prognostic biomarkers.

METHODS

We analyzed 740 tumor-progressive gene expression levels from formalin-fixed paraffin-embedded blocks of samples from six patients with low-risk PTC and six patients with high-risk PTC, using the nCounter PanCancer Progression panel. Then, we investigated the association between the expression levels of focused genes and pathological factors in PTC patients in The Cancer Genome Atlas (TCGA) database.

RESULTS

The expression levels of 14 genes in the high-risk PTC specimens were more than two-fold those in the low-risk PTC specimens. In the TCGA database, expression levels of four genes (CCL11, COL6A3, INHBA, and SRPX2) were significantly higher in patients with advanced PTC. Among the patients with advanced PTC, those with high SRPX2 expression levels had poor disease-free survival. Univariate and multivariate analyses revealed that high SRPX2 expression was an independent prognostic factor.

CONCLUSION

Based on the findings of this study, CCL11, COL6A3, INHBA, and SRPX2 are potential biomarkers that indicate advanced PTC. SRPX2, in particular, is considered a prognostic biomarker.

Human Mesenchymal Stem Cells-mediated Transcriptomic Regulation of Leukemic Cells in Delivering Anti-tumorigenic Effects

Treatment of leukemia has become much difficult because of resistance to the existing anticancer therapies. This has thus expedited the search for alternativ therapies, and one of these is the exploitation of mesenchymal stem cells (MSCs) towards control of tumor cells. The present study investigated the effect of human umbilical cord-derived MSCs (UC-MSCs) on the proliferation of leukemic cells and gauged the transcriptomic modulation and the signaling pathways potentially affected by UC-MSCs. The inhibition of growth of leukemic tumor cell lines was assessed by proliferation assays, apoptosis and cell cycle analysis. BV173 and HL-60 cells were further analyzed using microarray gene expression profiling. The microarray results were validated by RT-qPCR and western blot assay for the corresponding expression of genes and proteins. The UC-MSCs attenuated leukemic cell viability and proliferation in a dose-dependent manner without inducing apoptosis. Cell cycle analysis revealed that the growth of tumor cells was arrested at the G₀/G₁ phase. The microarray results identified that HL-60 and BV173 share 35 differentially expressed genes (DEGs) (same expression direction) in the presence of UC-MSCs. In silico analysis of these selected DEGs indicated a significant influence in the cell cycle and cell cycle-related biological processes and signaling pathways. Among these, the expression of DBF4, MDM2, CCNE2, CDK6, CDKN1A, and CDKN2A was implicated in six different signaling pathways that play a pivotal role in the anti-tumorigenic activity exerted by UC-MSCs. The UC-MSCs perturbate the cell cycle process of leukemic cells via dysregulation of tumor suppressor and oncogene expression.

High expression of bone morphogenetic protein 1 (BMP1) is associated with a poor survival rate in human gastric cancer, a dataset approaches

Bone morphogenetic protein 1 (BMP1) is a secreted metalloprotease of the astacin M12A family of bone morphogenetic proteins (BMPs). BMP1 activates transforming growth factor-β (TGF-β) and BMP signaling pathways by proteolytic cleavage, which has dual roles in gastrointestinal tumor development and progression.TGF-β promotes invasion and metastasis of gastric cancer (GC) by the help of BMP1, so upregulation of the BMP1 may increase cancer invasiveness in GC. In this study,the transcriptional expression, mutations, survival rate, TFs, miRNAs, gene ontology, and signaling pathways of BMP1 were analyzed by using different web servers. We found higher transcriptional and clinicopathological characteristics expression compared to normal tissues, worsening survival rate in GC. We detected 25 missenses, 15 truncating mutations, 23 TFs, and 8 miRNAs. Finally, we identified and analyzed the co-expressed genes and found that the leukemia inhibitory factor is the most positively correlated gene. The gene ontological features and signaling pathways involved in GC development were evaluated as well. We believe that this study will provide a basis for BMP1 to be a significant biomarker for human GC prognosis.

The Biological Role of the Collagen Alpha-3 (VI) Chain and Its Cleaved C5 Domain Fragment Endotrophin in Cancer

The collagen alpha-3 (VI) chain encoded by the gene COL6A3 is one of the 3 subunits of collagen VI which is a microfibrillar component of the extracellular matrix and is essential for the stable assembly process of collagen VI. The collagen alpha-3 (VI) chain and the cleaved C5 domain fragment, called endotrophin, are highly expressed in a variety of cancers and play a crucial role in cancer progression. The biological functions of endotrophin in tumors can be driven by adipocytes. Studies have demonstrated that endotrophin can directly affect the malignancy of cancer cells through TGF-β-dependent mechanisms, inducing epithelial–mesenchymal transition and fibrosis of the tumor microenvironment. In addition, endotrophin can also recruit macrophages and endothelial cells through chemotaxis to regulate the tumor microenvironment and ultimately promote tumor inflammation and angiogenesis. Furthermore, COL6A3 and endotrophin serve as novel diagnostic and prognostic biomarkers in cancer and contribute to clinical therapeutic applications in the future. In summary, in this review, we discuss the importance of the collagen alpha-3 (VI) chain and endotrophin in cancer progression, the future clinical applications of endotrophin and the remaining challenges in this field.

Prognostic value of COL6A3 in pancreatic adenocarcinoma

Backgrounds/Aims

Pancreatic cancer is one of the most fatal human malignancies with poor prognosis, despite advances in therapy. Here, we evaluated the potential role of collagen type VI α3 chain (COL6A3) as a non-invasive biomarker for pancreatic adenocarcinoma.

Methods

In this study, we investigated immunohistochemically the expression of COL6A3 in 30 patients with resectable pancreatic adenocarcinoma by immunohistochemistry in a tissue sample of the cancer and a tissue sample of normal pancreas for each patient. Also, we looked for associations between COL6A3 and other prognostic factors of pancreatic cancer.

Results

All of the pancreatic cancer tissue samples revealed in different ranges of intensity from weak (+) in 16.67%, moderate (+2) in 50%, to strongly positive (+3) in 33.33% staining for COL6A3. We found no moderate or strongly positive staining in normal pancreatic tissue. There was only weak positive staining in 23 samples (76.67%) and 7 (23.30%) were negative. Also, there was significant correlation between COL6A3 moderate and strongly expression and negative prognostic factors for pancreatic cancer.

Conclusions

The greatest density of COL6A3 was observed in pancreatic cancer tissues and was correlated with negative prognostic factors for pancreatic cancer. Therefore, we suggest that COL6A3 could be used as prognostic factor in pancreatic cancer, but more studies need to prove its value.

The Extracellular Matrix: An Accomplice in Gastric Cancer Development and Progression

The extracellular matrix (ECM) is a dynamic and highly organized tissue structure, providing support and maintaining normal epithelial architecture. In the last decade, increasing evidence has emerged demonstrating that alterations in ECM composition and assembly strongly affect cellular function and behavior. Even though the detailed mechanisms underlying cell-ECM crosstalk are yet to unravel, it is well established that ECM deregulation accompanies the development of many pathological conditions, such as gastric cancer. Notably, gastric cancer remains a worldwide concern, representing the third most frequent cause of cancer-associated deaths. Despite increased surveillance protocols, patients are usually diagnosed at advanced disease stages, urging the identification of novel diagnostic biomarkers and efficient therapeutic strategies. In this review, we provide a comprehensive overview regarding expression patterns of ECM components and cognate receptors described in normal gastric epithelium, pre-malignant lesions, and gastric carcinomas. Important insights are also discussed for the use of ECM-associated molecules as predictive biomarkers of the disease or as potential targets in gastric cancer.

Alteration and prognostic values of collagen gene expression in patients with gastric cancer under different treatments

Collagen (COL) genes participate in tumor extracellular matrix (ECM)-receptor interactions and focal adhesion pathways, which play a crucial role in tumor invasion and metastasis. The prognostic value of COL genes has been shown for several malignancies. In the present study, we analyzed multiple microarray datasets using the Oncomine database to identify alterations of COL genes in gastric cancer (GC). Gene expression levels were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) in GC tissues and matched adjacent tissues. The prognostic value of differentially expressed COL genes in GC was evaluated by Kaplan-Meier survival analysis based on the complete mRNA transcriptomics data from The Cancer Genome Atlas (TCGA). We found that seven COL genes (COL1A2, COL4A1, COL4A2, COL6A1, COL6A2, COL6A3, and COL11A1) were elevated in GC. Among them, stepwise multivariate Cox regression was applied, and it was determined that COL4A1 and COL4A2 were signature and independent prognostic biomarkers in GC patients with obviously different overall survival (OS). High expression of COL4A1, COL4A2, COL6A1, COL6A2, and COL6A3 was correlated with poorer prognosis of GC patients treated by surgery only, while higher expression of COL4A1 and COL11A1 correlated with poorer survival of patients treated by 5-fluorouracil-based adjuvant therapy. Our results indicate that overexpression of COL genes might be utilized as novel prognostic markers for GC and assist with therapy selection.

Genome-wide DNA copy number profiling and bioinformatics analysis of ovarian cancer reveals key genes and pathways associated with distinct invasive/migratory capabilities

Ovarian cancer (OC) metastasis presents major hurdles that must be overcome to improve patient outcomes. Recent studies have demonstrated copy number variations (CNVs) frequently contribute to alterations in oncogenic drivers. The present study used a CytoScan HD Array to analyse CNVs and loss of heterozygosity (LOH) in the entire genomes of 6 OC patients and human OC cell lines to determine the genetic target events leading to the distinct invasive/migratory capacities of OC. The results showed that LOH at Xq11.1 and Xp21.1 and gains at 8q21.13 were novel, specific CNVs. Ovarian cancer-related CNVs were then screened by bioinformatics analysis. In addition, transcription factors-target gene interactions were predicted with information from PASTAA analysis. As a result, six genes (i.e., GAB2, AKT1, EGFR, COL6A3, UGT1A1 and UGT1A8) were identified as strong candidates by integrating the above data with gene expression and clinical outcome data. In the transcriptional regulatory network, 4 known cancer-related transcription factors (TFs) interacted with 6 CNV-driven genes. The protein/DNA arrays revealed 3 of these 4 TFs as potential candidate gene-related transcription factors in OC. We then demonstrated that these six genes can serve as potential biomarkers for OC. Further studies are required to elucidate the pathogenesis of OC.

Omental Tissue-Mediated Tumorigenesis of Gastric Cancer Peritoneal Metastases

The peritoneal cavity, especially the omentum, is a common site for gastric cancer metastasis, representing advanced disease stage and poor prognosis. Here, we studied the effects of omental tissue on gastric cancer tumor progression in vitro and in vivo. Utilizing in vitro models, we found that omental tissue secreted factors increased gastric cancer cellular growth (by 30–67%, P < 0.05), motility (>8-fold, P < 0.05), invasiveness (>7-fold, P < 0.05) and chemoresistance to platinum-based chemotherapeutic agents (>1.2-fold for oxaliplatin and >1.6-fold for cisplatin, P < 0.05). Using a robust proteomic approach, we identified numerous molecules secreted into the omental tissue conditioned medium (CM) which may promote gastric cancer cellular aggressiveness (i.e., IL-6, IL-8, MMP9, FN1, and CXCL-5). Next, an in vivo xenograft mouse model showed an increased human gastric adenocarcinoma tumor volume of cells co-cultured with human omental tissue secreted factors; 1.6 ± 0.55 vs. 0.3 ± 0.19 cm³ (P < 0.001), as well as increased angiogenesis. Finally, exosomes were isolated from human omental tissue CM of gastric cancer patients. These exosomes were taken up by gastric cancer cells enhancing their growth (>8-fold, P < 0.01) and invasiveness (>8-fold, P < 0.001). Proteomic analysis of the content of these exosomes identified several established cancer- related proteins (i.e., IL-6, IL-8, ICAM-1, CCl2, and OSM). Taken together, our findings imply that the omentum play an active role in gastric cancer metastasis. The data also describe specific cytokines that are involved in this cross talk, and that omental tissue- derived exosomes may contribute to these unique cellular interactions with gastric cancer cells. Further studies aimed at understanding the biology of gastric cancer intra peritoneal spread are warranted. Hopefully, such data will enable to develop future novel therapeutic strategies for the treatment of metastatic gastric cancer.

Clinical significance of COL6A3 expression in gastric cancer

BACKGROUND

Elucidating the molecular mechanism of gastric cancer (GC) is of great significance, especially for improving the prognosis, and few studies have focused on the clinical significance of COL6A3 expression in GC.

AIM

To analyze the clinical significance of COL6A3 expression in GC, and to identify the potential role of COL6A3 in GC.

METHODS

The biological big data of GC were collected, and retrospective analysis and survival analysis were conducted on the expression profile of COL6A3 in tumor samples and its relationship with clinical data. Gene set enrichment analysis was used to identify the potential role of COL6A3 in GC.

RESULTS

COL6A3 was highly expressed in GC (P < 0.0001) with good diagnostic value (AUC = 0.9516). High COL6A3 expression was associated with deeper tumor invasion (P = 0.001). Patients with high expression of COL6A3 showed a poorer prognosis than those with low expression (P = 0.0018). The tumor-related gene sets were enriched in the samples with high COL6A3 expression.

CONCLUSION

The high expression of COL6A3 is related to the occurrence and development of GC.

Alternative PDGFD rearrangements in dermatofibrosarcomas protuberans without PDGFB fusions

Dermatofibrosarcoma protuberans is underlined by recurrent collagen type I alpha 1 chain-platelet-derived growth factor B chain (COL1A1-PDGFB) fusions but ~ 4% of typical dermatofibrosarcoma protuberans remain negative for this translocation in routine molecular screening. We investigated a series of 21 cases not associated with the pathognomonic COL1A1-PDGFB fusion on routine fluorescence in situ hybridization (FISH) testing. All cases displayed morphological and clinical features consistent with the diagnosis of dermatofibrosarcoma protuberans. RNA-sequencing analysis was successful in 20 cases. The classical COL1A1-PDGFB fusion was present in 40% of cases (n = 8/20), and subsequently confirmed with a COL1A1 break-apart FISH probe in all but one case (n = 7/8). 55% of cases (n = 11/20) displayed novel PDGFD rearrangements; PDGFD being fused either to the 5' part of COL6A3 (2q37.3) (n = 9/11) or EMILIN2 (18p11) (n = 2/11). All rearrangements led to in-frame fusion transcripts and were confirmed at genomic level by FISH and/or array-comparative genomic hybridization. PDGFD-rearranged dermatofibrosarcoma protuberans presented clinical outcomes similar to typical dermatofibrosarcoma protuberans. Notably, the two EMILIN2-PDGFD cases displayed fibrosarcomatous transformation and homozygous deletions of CDKN2A at genomic level. We report the first recurrent molecular variant of dermatofibrosarcoma protuberans involving PDGFD, which functionally mimic bona fide COL1A1-PDGFB fusions, leading presumably to a similar autocrine loop-stimulating PDGFRB. This study also emphasizes that COL1A1-PDGFB fusions can be cytogenetically cryptic on FISH testing in a subset of cases, thereby representing a diagnostic pitfall that pathologists should be aware of.

Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis

Background

Gastric cancer (GC) is a prevalent malignant cancer of digestive system. To identify key genes in GC, mRNA microarray GSE27342, GSE29272, and GSE33335 were downloaded from GEO database.

Methods

Differentially expressed genes (DEGs) were obtained using GEO2R. DAVID database was used to analyze function and pathways enrichment of DEGs. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software. Then, the influence of hub genes on overall survival (OS) was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Additionally, potential stem loop miRNAs of hub genes were predicted by miRecords and screened by TCGA dataset. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst.

Results

In total, 67 DEGs were identified; upregulated DEGs were mainly enriched in biological process (BP) related to angiogenesis and extracellular matrix organization and the downregulated DEGs were mainly enriched in BP related to ion transport and response to bacterium. KEGG pathways analysis showed that the upregulated DEGs were enriched in ECM-receptor interaction and the downregulated DEGs were enriched in gastric acid secretion. A PPI network of DEGs was constructed, consisting of 43 nodes and 87 edges. Twelve genes were considered as hub genes owing to high degrees in the network. Hsa-miR-29c, hsa-miR-30c, hsa-miR-335, hsa-miR-33b, and hsa-miR-101 might play a crucial role in hub genes regulation. In addition, the transcription factors-hub genes pairs were displayed with 182 edges and 102 nodes. The high expression of 7 out of 12 hub genes was associated with worse OS, including COL4A1, VCAN, THBS2, TIMP1, COL1A2, SERPINH1, and COL6A3.

Conclusions

The miRNA and TFs regulation network of hub genes in GC may promote understanding of the molecular mechanisms underlying the development of gastric cancer and provide potential targets for GC diagnosis and treatment.

Electronic supplementary material

The online version of this article (10.1186/s12957-018-1409-3) contains supplementary material, which is available to authorized users.

Genome-wide association pathway analysis to identify candidate single nucleotide polymorphisms and molecular pathways associated with TP53 expression status in HBV-related hepatocellular carcinoma

Background

The aim of this investigation was to identify candidate single nucleotide polymorphisms (SNPs) and molecular pathways associated with tumor protein p53 (TP53) expression status in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), clarify their potential mechanisms, and generate SNP-to-gene to pathway hypothesis.

Materials and methods

Identify candidate Causal SNPs and Pathways (ICSNPathway) was used to perform pathway analysis based on the results of our previous genome-wide association study of TP53 expression status in 387 HBV-related HCC patients.

Results

Through the ICSNPathway analysis, we identified 18 candidate SNPs and 10 candidate pathways that are associated with TP53 expression status in HBV-related HCC. The strongest mechanism involved the modulation of major histocompatibility complex, class II, DP beta 1 (human leukocyte antigen [HLA]-DPB1-rs1042153), major histocompatibility complex, class II, DQ beta 1 (HLA-DQB1-rs1130399, HLA-DQB1-rs1049056, HLA-DQB1-rs1049059, and HLA-DQB1-rs1049060), and major histocompatibility complex, class II, DR beta 1 (HLA-DRB1-rs35445101). SNPs consequently affected regulatory roles in all the candidate pathways except hematopoietic cell lineage pathways. Association analysis using the GSE14520 data set, Gene Multiple Association Network Integration Algorithm, and Search Tool for the Retrieval of Interacting Genes/Proteins suggests that all genes of the candidate SNPs were associated with TP53. Survival analysis showed that the collagen type VI alpha 3 chain (COL6A3) rs111231885 and COL6A3-rs113155945 and COL6A3 block 4 CC haplotypes with TP53 negative status may have protective effects in HBV-related HCC patients after hepatectomy.

Conclusion

Our pathway analysis identified 18 candidate SNPs and 10 candidate pathways that were associated with TP53 expression status in HBV-related HCC. Among these candidate SNPs, the genetic variation of COL6A3 may be a potential prognostic biomarker of HBV-related HCC.

Silencing of COL1A2, COL6A3, and THBS2 inhibits gastric cancer cell proliferation, migration, and invasion while promoting apoptosis through the PI3k-Akt signaling pathway

This study explores the effect of COL1A2, COL6A3, and THBS2 gene silencing on proliferation, migration, invasion, and apoptosis of gastric cancer cells through the PI3K-Akt signaling pathway. The gastric cancer microarray expression data (GSE19826, GSE79973, and GSE65801) was analyzed. Gastric cancer tissues and corresponding adjacent normal tissues were extracted from patients. Positive expression rate of PI3K, Akt, and p-Akt was measured with immunohistochemistry. Two cell lines, BGC-823 and SGC-7901, were transfected and cells were grouped into blank, negative control, COL1A2-shRNA, COL6A3-shRNA, and THBS2-shRNA groups. Expressions of COL1A2, COL6A3, and THBS2 in gastric cancer cells transfected with corresponding silencing sequences were evaluated by RT-qPCR and Western blot. MTT assay, Transwell, and cell scratch tests were conducted to evaluate cell proliferation, invasion, and migration capacity, respectively. Flow cytometry was used to evaluate cell cycle distribution and apoptosis. The positive expression of PI3K, Akt, and p-Akt was higher in gastric cancer tissues compared with adjacent normal tissues, and the mRNA expression of COL1A2, COL6A3, and THBS2 was increased in gastric cancer tissues. Akt, p-Akt, and PI3K expression drastically decreased in cells transfected with COL1A2, COL6A3, and THBS2 silencing sequences. Cells transfected with COL1A2, COL6A3, and THBS2 silencing sequences exhibited promoted apoptosis but inhibited proliferation, migration, and invasion. This study demonstrates that COL1A2, COL6A3, and THBS2 gene silencing inhibits gastric cancer cell proliferation, migration, and invasion while promoting apoptosis through the PI3K-Akt signaling pathway.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.

Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

Differential expression of alternatively spliced transcripts related to energy metabolism in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. CRC molecular pathogenesis is heterogeneous and may be followed by mutations in oncogenes and tumor suppressor genes, chromosomal and microsatellite instability, alternative splicing alterations, hypermethylation of CpG islands, oxidative stress, impairment of different signaling pathways and energy metabolism. In the present work, we have studied the alterations of alternative splicing patterns of genes related to energy metabolism in CRC.

RESULTS

Using CrossHub software, we analyzed The Cancer Genome Atlas (TCGA) RNA-Seq datasets derived from colon tumor and matched normal tissues. The expression of 1014 alternative mRNA isoforms involved in cell energy metabolism was examined. We found 7 genes with differentially expressed alternative transcripts whereas overall expression of these genes was not significantly altered in CRC. A set of 8 differentially expressed transcripts of interest has been validated by qPCR. These eight isoforms encoded by OGDH, COL6A3, ICAM1, PHPT1, PPP2R5D, SLC29A1, and TRIB3 genes were up-regulated in colorectal tumors, and this is in concordance with the bioinformatics data. The alternative transcript NM_057167 of COL6A3 was also strongly up-regulated in breast, lung, prostate, and kidney tumors. Alternative transcript of SLC29A1 (NM_001078177) was up-regulated only in CRC samples, but not in the other tested tumor types.

CONCLUSIONS

We identified tumor-specific expression of alternative spliced transcripts of seven genes involved in energy metabolism in CRC. Our results bring new knowledge on alternative splicing in colorectal cancer and suggest a set of mRNA isoforms that could be used for cancer diagnosis and development of treatment methods.

HMGA1P7-pseudogene regulates H19 and Igf2 expression by a competitive endogenous RNA mechanism

Recent studies have revealed that pseudogene transcripts can function as competing endogenous RNAs, and thereby can also contribute to cancer when dysregulated. We have recently identified two pseudogenes, HMGA1P6 and HMGA1P7 for the HMGA1 gene whose overexpression has a critical role in cancer progression. These pseudogenes work as competitive endogenous RNA decoys for HMGA1 and other cancer related genes suggesting their role in carcinogenesis. Looking for new HMGA1 pseudogene ceRNAs, we performed RNA sequencing technology on mouse embryonic fibroblasts deriving from transgenic mice overexpressing HMGA1P7. Here, we report that HMGA1P7 mRNA sustains the H19 and Igf2 overexpression by acting as miRNA decoy. Lastly, the expression of HMGA1P7 was significantly correlated with H19 and IGF2 levels in human breast cancer thereby suggesting a role for HMGA1P7 deregulation in this neoplasia.

Commonality amid diversity: Multi-study proteomic identification of conserved disease mechanisms in spinal muscular atrophy

The neuromuscular disease spinal muscular atrophy (SMA) is a leading genetic cause of infant mortality, resulting from low levels of full-length survival motor neuron (SMN) protein. Despite having a good understanding of the underlying genetics of SMA, the molecular pathways downstream of SMN that regulate disease pathogenesis remain unclear. The identification of molecular perturbations downstream of SMN is required in order to fully understand the fundamental biological role(s) for SMN in cells and tissues of the body, as well as to develop a range of therapeutic targets for developing novel treatments for SMA. Recent developments in proteomic screening technologies have facilitated proteome-wide investigations of a range of SMA models and tissues, generating novel insights into disease mechanisms by highlighting conserved changes in a range of molecular pathways. Comparative analysis of distinct proteomic datasets reveals conserved changes in pathways converging on GAP43, GAPDH, NCAM, UBA1, LMNA, ANXA2 and COL6A3. Proteomic studies therefore represent a leading tool with which to dissect the molecular mechanisms of disease pathogenesis in SMA, serving to identify potentially attractive targets for the development of novel therapies.

iTRAQ Quantitative Proteomic Comparison of Metastatic and Non-Metastatic Uveal Melanoma Tumors

BACKGROUND

Uveal melanoma is the most common malignancy of the adult eye. The overall mortality rate is high because this aggressive cancer often metastasizes before ophthalmic diagnosis. Quantitative proteomic analysis of primary metastasizing and non-metastasizing tumors was pursued for insights into mechanisms and biomarkers of uveal melanoma metastasis.

METHODS

Eight metastatic and 7 non-metastatic human primary uveal melanoma tumors were analyzed by LC MS/MS iTRAQ technology with Bruch's membrane/choroid complex from normal postmortem eyes as control tissue. Tryptic peptides from tumor and control proteins were labeled with iTRAQ tags, fractionated by cation exchange chromatography, and analyzed by LC MS/MS. Protein identification utilized the Mascot search engine and the human Uni-Prot/Swiss-Protein database with false discovery ≤ 1%; protein quantitation utilized the Mascot weighted average method. Proteins designated differentially expressed exhibited quantitative differences (p ≤ 0.05, t-test) in a training set of five metastatic and five non-metastatic tumors. Logistic regression models developed from the training set were used to classify the metastatic status of five independent tumors.

RESULTS

Of 1644 proteins identified and quantified in 5 metastatic and 5 non-metastatic tumors, 12 proteins were found uniquely in ≥ 3 metastatic tumors, 28 were found significantly elevated and 30 significantly decreased only in metastatic tumors, and 31 were designated differentially expressed between metastatic and non-metastatic tumors. Logistic regression modeling of differentially expressed collagen alpha-3(VI) and heat shock protein beta-1 allowed correct prediction of metastasis status for each of five independent tumor specimens.

CONCLUSIONS

The present data provide new clues to molecular differences in metastatic and non-metastatic uveal melanoma tumors. While sample size is limited and validation required, the results support collagen alpha-3(VI) and heat shock protein beta-1 as candidate biomarkers of uveal melanoma metastasis and establish a quantitative proteomic database for uveal melanoma primary tumors.