Identification of Key Genes and Circular RNAs in Human Gastric Cancer

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

CircSI-SSL: circRNA-binding site identification based on self-supervised learning

MOTIVATION

In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain.

RESULTS

To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/cc646201081/CircSI-SSL.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

SPARC: a potential target for functional nanomaterials and drugs

Secreted protein acidic and rich in cysteine (SPARC), also termed osteonectin or BM-40, is a matricellular protein which regulates cell adhesion, extracellular matrix production, growth factor activity, and cell cycle. Although SPARC does not perform a structural function, it, however, modulates interactions between cells and the surrounding extracellular matrix due to its anti-proliferative and anti-adhesion properties. The overexpression of SPARC at sites, including injury, regeneration, obesity, cancer, and inflammation, reveals its application as a prospective target and therapeutic indicator in the treatment and assessment of disease. This article comprehensively summarizes the mechanism of SPARC overexpression in inflammation and tumors as well as the latest research progress of functional nanomaterials in the therapy of rheumatoid arthritis and tumors by manipulating SPARC as a new target. This article provides ideas for using functional nanomaterials to treat inflammatory diseases through the SPARC target. The purpose of this article is to provide a reference for ongoing disease research based on SPARC-targeted therapy.

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

BACKGROUND

Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification usually adopt convolution neural network (CNN), recurrent neural network (RNN), or their variants as feature extractors. Most of them have drawbacks such as poor parallelism, insufficient stability, and inability to capture long-term dependencies.

METHODS

In this paper, we propose a new method completely using the self-attention mechanism to capture deep semantic features of RNA sequences. On this basis, we construct a CircSSNN model for the cirRNA-RBP identification. The proposed model constructs a feature scheme by fusing circRNA sequence representations with statistical distributions, static local contexts, and dynamic global contexts. With a stable and efficient network architecture, the distance between any two positions in a sequence is reduced to a constant, so CircSSNN can quickly capture the long-term dependencies and extract the deep semantic features.

RESULTS

Experiments on 37 circRNA datasets show that the proposed model has overall advantages in stability, parallelism, and prediction performance. Keeping the network structure and hyperparameters unchanged, we directly apply the CircSSNN to linRNA datasets. The favorable results show that CircSSNN can be transformed simply and efficiently without task-oriented tuning.

CONCLUSIONS

In conclusion, CircSSNN can serve as an appealing circRNA-RBP identification tool with good identification performance, excellent scalability, and wide application scope without the need for task-oriented fine-tuning of parameters, which is expected to reduce the professional threshold required for hyperparameter tuning in bioinformatics analysis.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Gastric cancer (GC) ranks third for cancer-related fatalities worldwide. It is still unclear what causes GC to progress. Using integrated bioinformatics analysis, COL5A2 has been proved to be related to GC development, which may identify the likely pathogenic mechanism. Data from GC patients were gathered using The Cancer Gene Atlas (TCGA) and the gene expression omnibus (GEO). The level of COL5A2 expression was compared between paired GC and normal tissues. The differentially expressed genes (DEGs) in GC patients with high and low COL5A2 expression were identified using functional enrichment analysis to identify the signature pathways linked to the DEGs. The clinical pathologic traits connected to overall survival (OS) of GC patients were examined utilizing Cox regression and the Kaplan-Meier method. To assess the prognostic significance of COL5A2, receiver operating characteristic (ROC) curves was drawn. How the immune system infiltrate both normal gastric and GC tumor tissues was investigated. Using the human protein atlas (HPA) database, regression, and the Kaplan-Meier method, immunohistochemical analysis of DEG COL5A2 expression in GC tissues was carried out. The correlation between COL5A2 expression and the GC grouping was found to be highly significant. Functional annotations revealed that COL5A2 participates in extracellular matrix structure, collagen metabolism, and other biological processes (BPs). High COL5A2 expression was associated with poor prognostic and clinical features, such as clinical T, N, and M stages. ROC curves exhibited that COL5A2 might predict the occurrence of gastric cancer. The infiltration degree of 21 immune cell subsets, including activated dendritic cells (aDCs), CD8+ T cells, and cytotoxic cells, was found to be dramatically relevant to COL5A2. Immunohistochemical analysis indicated that the expression of COL5A2 in tumor tissues is higher than that in normal tissues. The COL5A2 gene may offer fresh perspectives on the pathogenic mechanism underlying GC, as well as potential biomarkers for estimating GC patient prognosis. As a result, COL5A2 may be a useful biomarker for predicting patient survival.

Comprehensive Analysis of N6-Methyladenosine (m6A) RNA Methylation Regulators and Tumour Microenvironment Cell Infiltration Involving Prognosis and Immunotherapy in Gastroesophageal Adenocarcinomas

Objective

Gastroesophageal adenocarcinoma (GEA) is a high deadly and heterogeneous cancer. RNA N6-methyladenosine (m6A) modification plays a non-negligible role in shaping individual tumour microenvironment (TME) characterizations. However, the landscape and relationship of m6A modification patterns and TME cell infiltration features remain unknown in GEA.

Methods

In this study, we examined the TME of GEA using assessments of the RNA-sequencing data focusing on the distinct m6A modification patterns from the public databases. Intrinsic patterns of m6A modification were evaluated for associations with clinicopathological characteristics, underlying biological pathways, tumour immune cell infiltration, oncological outcomes, and treatment responses. The expression of key m6A regulators and module genes was validated by qRT-PCR analysis.

Results

We identified two distinct m6A modification patterns of GEA (cluster 1/2 subgroup), and correlated two subgroups with TME cell-infiltrating characteristics. Cluster 2 subgroup correlates with a poorer prognosis, downregulated PD-1 expression, higher risk scores, and distinct immune cell infiltration. In addition, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. COL4A1 and COL5A2 in the brown module were significantly correlated to the prognosis, PD-1/L1 and CTLA-4 expression of GEA patients. Finally, a prognostic risk score was constructed using m6A regulator-associated signatures that represented an independent prognosis factor for GEA. Interestingly, COL5A2 expression was linked to the response to anti-PD-1 immunotherapy, m6A regulator expression, and risk score.

Conclusion

Our work identified m6A RNA methylation regulators as an important class of players in the malignant progression of GEA and were associated with the complexity of the TME. COL5A2 may be the potential biomarker which contributes to predicting the response to anti-PD-1 immunotherapy and patients' prognosis.

Melatonin may suppress lung adenocarcinoma progression via regulation of the circular noncoding RNA hsa_circ_0017109/miR-135b-3p/TOX3 axis

Melatonin is a hormone synthesized in the pineal gland and has widespread physiological and pharmacological functions. Moreover, it can activate protective receptor-dependent processes. These processes can prevent tissue carcinogenesis and inhibit malignant tumor progression and metastasis. Therefore, we investigated the regulatory effects of melatonin on dysregulated circular RNAs in human lung adenocarcinoma (LUAD) cells. In this study, we treated LUAD cells with melatonin and measured the expression of hsa_circ_0017109, miR-135b-3p, and TOX3 by quantitative reverse transcription polymerase chain reaction. Colony formation and cell counting kit-8 assays were used to determine cell proliferation. The wound-healing assay and Transwell experiment were carried out to evaluate the migration potential and invasive capacity of LUAD cells. Also, cell apoptosis was detected using a cell apoptosis kit, and protein production was identified by Western blot. It was suggested that melatonin could inhibit LUAD progression in vivo and in vitro, and the role of TOX3 in this process was explored. Additionally, hsa_circ_0017109 was found to sponge miR-135b-3p, a downstream factor of circ_0017109, which was demonstrated to target TOX3 in LUAD cells and could promote the Hippo pathway and epithelial-mesenchymal transition pathway. To summarize, we demonstrated that melatonin decreases the expression of circ_0017109 and suppresses the non-small-cell lung cancer cell migration, invasion, and proliferation through decreasing TOX3 expression via direct activation of miR-135b-3p.

Circular RNA circLMO1 Suppresses Cervical Cancer Growth and Metastasis by Triggering miR-4291/ACSL4-Mediated Ferroptosis

Background

A number of studies have demonstrated that circular RNA (circRNA) plays a critical role in tumorigenesis and tumor progression. However, the biological effects of most circRNAs on cervical cancer remain unclear. Hsa_circ_0021087 (thereafter named circLMO1) is a circRNA generated from the circularization of exon 2 and exon 3 of LIM Domain Only 1 (LMO1) and first identified as a tumor suppressor in gastric cancer. We aimed to identify the role of circLMO1 in cervical cancer progression.

Methods

CircLMO1 was verified through qPCR and Sanger sequencing. The biological role of circLMO1 in regulating cervical cancer growth and metastasis was investigated both in vitro and in the nude mouse xenograft tumor model. The dual luciferase reporter assay and rescue experiment were conducted to evaluate the interactions among circLMO1, microRNA (miR)-4291, and acyl-CoA synthetase long chain family member 4 (ACSL4). The role of circLMO1 in regulating ferroptosis was assessed by analyzing lipid reactive oxygen species (ROS), and malonyl dialdehyde (MDA), and glutathione (GSH) content.

Results

The level of circLMO1 was down-regulated in cervical cancer tissues and was associated with the International Federation of Gynecology and Obstetrics (FIGO) staging. Functionally, circLMO1 overexpression inhibited cervical cancer growth and metastasis both in vitro and in vivo, whereas circLMO1 depletion promoted cervical cancer cell proliferation and invasion. Mechanistically, circLMO1 acted as a competing endogenous RNA (ceRNA) by sponging miR-4192 to repress target gene ACSL4. CircLMO1 promoted cervical cancer cell ferroptosis through up-regulating ACSL4 expression. Overexpression of miR-4291 or knockdown of ACSL4 reversed the effect of circLMO1 on facilitating ferroptosis and repressing cervical cancer cell proliferation and invasion.

Conclusion

CircLMO1 acted as a tumor suppressor of cervical cancer by regulating miR-4291/ACSL4-mediated ferroptosis, and could be a promising biomarker for the clinical management of cervical cancer.

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments.

CircCNIH4 inhibits gastric cancer progression via regulating DKK2 and FRZB expression and Wnt/β-catenin pathway

Background

Circular RNAs (circRNAs) have been reported to play an important role in tumor progression in various cancer types, including gastric cancer. The aim of this study was to investigate the role of circCNIH4 (hsa_circ_0000190) in gastric cancer and the underlying mechanism.

Methods

The expression levels of circCNIH4 and Wnt antagonist genes were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The protein levels of β-catenin, Ki67, Dickkopf 2 (DKK2) and Frizzled related protein (FRZB) were measured by western blot. Ectopic overexpression or knockdown of circCNIH4, proliferation, apoptosis, migration and invasion by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT), flow cytometry and transwell assay in vitro, and in vivo experiment, were employed to assess the role of circCNIH4 in gastric cancer.

Results

CircCNIH4 was downregulated in gastric cancer tissues and cells. Overexpression of circCNIH4 inhibited gastric cancer cell proliferation, migration and invasion and promoted apoptosis by inactivating Wnt/β-catenin pathway in vitro. CircCNIH4 induced the expression of DKK2 and FRZB in gastric cancer cells. Moreover, silencing of DKK2 or FRZB reversed circCNIH4 overexpression-mediated effects on gastric cancer cells. Additionally, circCNIH4 suppressed tumor growth via regulating DKK2 and FRZB expression in gastric cancer in vivo.

Conclusion

Our study demonstrated that circCNIH4 played a tumor-inhibiting role through upregulating DKK2 and FRZB expression and suppressing Wnt/β-catenin pathway in gastric cancer, which might provide a potential biomarker for the diagnosis and treatment of gastric cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40709-021-00140-x.

Collagen XI alpha 1 chain, a potential therapeutic target for cancer

Extracellular matrix (ECM) plays an important role in the progression of cancer. Collagen is the most abundant component in ECM, and it is involved in the biological formation of cancer. Although type XI collagen is a minor fibrillar collagen, collagen XI alpha 1 chain (COL11A1) has been found to be upregulated in a variety of cancers including ovarian cancer, breast cancer, thyroid cancer, pancreatic cancer, non-small-cell lung cancer, and transitional cell carcinoma of the bladder. High levels of COL11A1 usually predict poor prognosis, while COL11A1 is related to angiogenesis, invasion, and drug resistance of cancer. However, little is known about the specific mechanism by which COL11A1 regulates tumor progression. Here, we have organized and summarized the recent developments regarding elucidation of the relationship between COL11A1 and various cancers, as well as the interaction between COL11A1 and intracellular signaling pathways. In addition, we have selected therapeutic agents targeting COL11A1. All these indicate the possibility of using COL11A1 as a target for cancer treatment.

Identification of Potential Bisphenol A (BPA) Exposure Biomarkers in Ovarian Cancer

Endocrine-disrupting chemicals (EDCs) can exert multiple deleterious effects and have been implicated in carcinogenesis. The xenoestrogen Bisphenol A (BPA) that is found in various consumer products has been involved in the dysregulation of numerous signalling pathways. In this paper, we present the analysis of a set of 94 genes that have been shown to be dysregulated in presence of BPA in ovarian cancer cell lines since we hypothesised that these genes might be of biomarker potential. This study sought to identify biomarkers of disease and biomarkers of disease-associated exposure. In silico analyses took place using gene expression data extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Differential expression was further validated at protein level using immunohistochemistry on an ovarian cancer tissue microarray. We found that 14 out of 94 genes are solely dysregulated in the presence of BPA, while the remaining 80 genes are already dysregulated (p-value < 0.05) in their expression pattern as a consequence of the disease. We also found that seven genes have prognostic power for the overall survival in OC in relation to their expression levels. Out of these seven genes, Keratin 4 (KRT4) appears to be a biomarker of exposure-associated ovarian cancer, whereas Guanylate Binding Protein 5 (GBP5), long intergenic non-protein coding RNA 707 (LINC00707) and Solute Carrier Family 4 Member 11 (SLC4A11) are biomarkers of disease. BPA can exert a plethora of effects that can be tissue- or cancer-specific. Our in silico findings generate a hypothesis around biomarkers of disease and exposure that could potentially inform regulation and policy making.

The silencing of FNDC1 inhibits the tumorigenesis of breast cancer cells via modulation of the PI3K/Akt signaling pathway

Fibronectin type III domain-containing protein 1 (FNDC1) is a protein that contains a major component of the structural domain of fibronectin. Although many studies have indicated that FNDC1 serves vital roles in the development of various diseases, the role of FNDC1 in the progression of breast cancer (BC) remains elusive. The aim of the present study was to investigate the biological functions of FNDC1 in BC cells and the associated mechanisms. The expression levels of FNDC1 in BC tissues and normal breast tissues were analyzed using The Cancer Genome Atlas database (TCGA). Kaplan-Meier curves were mined from TCGA to examine the clinical prognostic significance of FNDC1 mRNA in patients with BC. The expression of FNDC1 was knocked down by transfection with shRNA in BC cells. Cell viability, colony formation ability, migration and invasion were assayed following the silencing of FNDC1 in BC cells. The expression of proteins was measured using a western blotting assay. The bioinformatic data indicated that the FNDC1 mRNA expression levels were significantly upregulated in BC tissues compared with normal breast tissues, and the high mRNA expression levels of FNDC1 were associated with a lower overall survival in patients with BC. The downregulation of FNDC1 inhibited the proliferation, colony formation, migration and invasion of BC cells. Investigation of the mechanisms revealed that the silencing of FNDC1 decreased the protein expression levels of MMPs and epithelial-to-mesenchymal markers. Furthermore, the silencing of FNDC1 led to the inactivation of the PI3K/Akt signaling pathway. FNDC1 was highly upregulated and acted as an oncogene in BC. Therefore, targeting FNDC1 may be a potential strategy for the treatment of BC.

RUNX2 promotes malignant progression in gastric cancer by regulating COL1A1

BACKGROUND

Runt-related transcription factor 2 (RUNX2) is an important gene that has been implicated in the progression of human cancer. Aberrant expression of RUNX2 predicts gastric cancer (GC) metastasis. However, the molecular mechanism of RUNX2 remains unknown.

OBJECTIVE

We hypothesize that RUNX2 promotes GC metastasis by regulating the extracellular matrix component collagen type I alpha 1 (COL1A1).

METHODS

The GEPIA database and immunohistochemical staining of 60 GC tissues were used to analyse the correlations between RUNX2 or COL1A1 expression and clinicopathological features, and the Kaplan-Meier method was used to evaluate survival. RT-PCR, western blotting and immunofluorescence were used to detect RUNX2 and COL1A1 expression in GC cells. Migration and invasion assays were performed to assess the influence of RUNX2 and COL1A1 on metastasis.

RESULTS

RUNX2 and COL1A1 were highly expressed at both the gene and protein levels in GC, and patients who were positive for RUNX2 and COL1A1 had shorter survival. RUNX2 and COL1A1 expression linearly correlated with each other (r= 0.15, p< 0.01) and with clinical stage and lymph node metastasis (p< 0.05). Overexpressing RUNX2in vitro enhanced COL1A1 expression and promoted GC cell invasion and migration, whereas COL1A1 knockdown inhibited the increase in cell metastatic capacity promoted by RUNX2. In vivo, GC cells overexpressing RUNX2 promoted lung metastasis, and the downregulation of COL1A1 reduced the metastasis promoted by RUNX2.

CONCLUSIONS

RUNX2 may promote GC metastasis by regulating COL1A1. RUNX2/COL1A1 can be employed as a novel target for therapy in GC.

Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.

Circular RNA circ_0020123 Promotes Non-Small Cell Lung Cancer Progression Through miR-384/TRIM44 Axis

Background

It was reported that circular RNAs (circRNAs) and microRNAs (miRNAs) were related to non-small cell lung cancer (NSCLC) development. However, the detailed mechanisms of circ_0020123 and miR-384 in NSCLC are elusive.

Methods

QRT-PCR and Western blot assay were performed to detect the transcription and protein levels of genes, respectively. Then, the functional experiments, including MTT assay, flow cytometry, and transwell assay, were employed. Besides, the interaction between miR-384 and circ_0020123 or tripartite motif‑containing protein 44 (TRIM44) was predicted by starbase or targetscan, and then verified by the dual-luciferase reporter, RNA pull-down assays and RNA immunoprecipitation assay (RIP). Mouse xenograft assay was performed to evaluate the effect of circ_0020123 on tumor growth in vivo.

Results

Levels of circ_0020123 and TRIM44 were enhanced, and the miR-384 level was attenuated in NSCLC tissues and cells. Circ_0020123 depletion attenuated the abilities of NSCLC cell viability, migration, invasion, and epithelial–mesenchymal transition (EMT), and induced apoptosis. Besides, circ_0020123 interacted with miR-384, and miR-384 targeted TRIM44. Circ_0020123 regulated cell progression by regulating miR-384 and subsequently mediated TRIM44 expression. Besides, circ_0020123 depletion repressed tumor growth in vivo.

Conclusion

We demonstrated that circ_0020123 knockdown suppressed NSCLC cell progression by regulating the miR-384/TRIM44 axis, providing the theoretical basis for the therapy of NSCLC.

Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis

Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What’s more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.

A qualitative classification signature for post-surgery 5-fluorouracil-based adjuvant chemoradiotherapy in gastric cancer

BACKGROUND AND PURPOSE

Currently, 5-fluorouracil (5-FU)-based adjuvant chemoradiotherapy (ACRT) is a preferred regimen for post-surgery gastric cancer (GC). However, the survival outcome of 5-FU-based ACRT varies greatly among different GC patients. Thus, it is necessary to classify which patients may benefit from 5-FU-based ACRT.

MATERIALS AND METHODS

We collected 577 GC and 84 adjacent normal samples for training and 675 GC samples for validation. Based on the within-sample relative expression orderings (REOs) of gene expression levels, reversal gene pairs were selected, and the pairs correlating with overall survival (OS) of GC patients receiving 5-FU-based ACRT were identified as candidates. Finally, an optimized set of candidate gene pairs was selected as a classification signature in training data and validated in validation data.

RESULTS

A signature consisting of 34 gene pairs was identified in training data and validated in three independent datasets. The classified low-risk group had better OS than the classified high-risk group. We also analyzed the recurrent free survival or disease free survival (RFS/DFS) of the validation datasets, and the similar results were shown. Furthermore, although the signature was identified based on the OS of GC patients receiving ACRT, it was not a prognostic signature for patients treated with surgery alone, but may be a potential signature for 5-FU-based chemotherapy alone.

CONCLUSIONS

The signature can accurately classify GC patients who may benefit from 5-FU-based ACRT, which could aid clinicians in tailoring more effective GC treatments.

MetaDE-Based Analysis of circRNA Expression Profiles Involved in Gastric Cancer

BACKGROUND

Circular RNAs (circRNAs) could play carcinogenic roles in gastric cancer (GC) and have potential to be biomarkers for GC early diagnosis, which needs to be further excavated and supported by more evidence.

AIMS

The study aims to identify more authentic circRNA expression profiles that could function as potential biomarkers in GC.

METHODS

circRNA expression data in three microarrays were downloaded from Gene Expression Omnibus datasets. A systematic meta-analysis based on an integrated dataset pre-processed from the three microarrays was conducted to identify a panel of differentially expressed circRNAs (DEcircs) by using the metaDE package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes term enrichment were used to note the corresponding functions of DEcircs. Quantitative real-time polymerase chain reaction was applied to verify the DEcircs expression in cancer tissues and adjacent paracancerous tissues. A GC risk-related circRNAs-miRNAs-mRNAs network was further constructed and analyzed.

RESULTS

MetaDE analysis suggested 64 DEcircs between cancer tissues and adjacent normal tissues. GO and KEGG analysis showed that the parental genes of these DEcircs were mainly associated with histone methylation, Wnt signalosome and histone methylation activity. Hsa_circ_0005927 and hsa_circ_0067934 were verified in GC tissues, and a GC risk-related network was constructed.

CONCLUSION

MetaDE-based circRNA expression profiles revealed a series of potential biomarkers involved in GC. Two circRNAs, hsa_circ_0005927 and hsa_circ_0067934, could be more authentic biomarkers for GC screening. The GC risk-related network of hsa_circ_0005927/hsa_circ_0067934 and their downstream targets will provide new genetic insights for GC research.

Circular RNAs as New Regulators in Gastric Cancer: Diagnosis and Cancer Therapy

Gastric cancer (GC) is one of the most commonly diagnosed cancers that causes high mortality in the world. Although the surgery tools and chemotherapies have significantly improved the overall survival of patients with GC, the early diagnosis of GC remains insufficient and many patients diagnosed with advanced stages of GC are not able to benefit from curative therapy. Circular RNAs (circRNAs), novel members of the non-coding cancer genome, are being explored with regards to various cancer types including GC. CircRNAs could work as miRNA sponges to regulate cell proliferation, cell migration, and cell cycle in GC. In addition, it was found that abnormal expression of circRNAs was associated with pathological characteristics in GC tissues, which could help to act as potential markers of early diagnosis or predictors of prognosis. Although various functional circRNAs have been discovered and characterized, the studies of circRNAs in GC are still at early stages compared with other RNAs. In order to provide a whole view to better understand the circRNAs in the occurrence and development of GC, we review the current knowledge on circRNAs in relation to their expression and regulation in GC as well as their potential to be diagnosis markers, and their role in drug resistance will be mentioned. It is helpful to address their possibility from basic research into practical application.

Identification of a nine-gene prognostic signature for gastric carcinoma using integrated bioinformatics analyses

BACKGROUND

Gastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.

AIM

To identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.

METHODS

Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.

RESULTS

A total of 95 overlapping DEGs were found and a nine-gene signature (COL8A1, CTHRC1, COL5A2, AADAC, MAMDC2, SERPINE1, MAOA, COL1A2, and FNDC1) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas-stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.

CONCLUSION

The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.

SPARC-p53: The double agents of cancer

Cancer is a complex disease with high incidence and mortality rates. The important role played by the tumor microenvironment in regulating oncogenesis, tumor growth, and metastasis is by now well accepted in the scientific community. SPARC is known to participate in tumor-stromal interactions and impact cancer growth in ambiguous ways, which either enhance or suppress cancer aggressiveness, in a context-dependent manner. p53 transcription factor, a well-established tumor suppressor, has been reported to promote tumor growth in certain situations, such as hypoxia, thus displaying a duality in its action. Although both proteins are being tested in clinical trials, the synergistic relation between them is yet to be explored in clinical practice. In this review, we address the controversial roles of SPARC and p53 as double agents in cancer, briefly summarizing the interaction found between these two molecules and its importance in cancer.

H19 promotes the gastric carcinogenesis by sponging miR-29a-3p: evidence from lncRNA-miRNA-mRNA network analysis

Aim: To identify novel competing endogenous RNA (ceRNA) network correlated with the prognosis of gastric cancer (GC) patients. Materials & methods: We systematically analyzed the aberrantly expressed genes in human GC to construct a ceRNA network by using multiple bioinformatic tools. Results: Aberrantly expressed mRNAs in GC were identified. By means of stepwise reverse prediction and validation from mRNA to lncRNA, a ceRNA network comprised of H19, miR-29a-3p, COL3A1, COL5A2, COL1A2 and COL4A1 was constructed, and all genes in the network are significantly correlated with the prognosis of GC patients. Conclusion: The present study successfully constructed a GC related ceRNA network, and provided potential targets for GC clinical treatment.

Circular RNA HIPK3: A Key Circular RNA in a Variety of Human Cancers

Circular RNAs (circRNAs), which act as initiators and promoters of various diseases, were thought to be mostly noncoding RNAs (ncRNAs) in eukaryotes, until recent studies confirmed that some circRNAs have the function of encoding proteins. Accumulating research findings have proved that dysregulation of circRNAs is associated with the developmental process of multiple cancers. circHIPK3, an example of circRNA, is frequently expressed in many diseases, such as diabetes, age-related cataract, idiopathic pulmonary fibrosis, preeclampsia, osteoblasts, and retinal vascular dysfunction, leading to disease development and progression. In addition, circHIPK3 may also serve as a potential biomarker, to help us know more about the rules of occurrence and development of cancers. In recent studies, many circHIPK3-related cancers have been identified, including nasopharyngeal carcinoma, gallbladder cancer, lung cancer, hepatocellular carcinoma, osteosarcoma, glioma, colorectal cancer, ovarian cancer, bladder cancer, prostate cancer, gastric cancer, oral squamous cell carcinoma, and chronic myeloid leukemia. This review summarizes recent studies on the biological mechanisms of circHIPK3 and expounds the molecular mechanisms of circHIPK3 in these malignant tumors.

The Effect of Long Non-Coding RNA (lncRNA) HCP5 on Regulating Epithelial-Mesenchymal Transition (EMT)-Related Markers in Gastric Carcinoma Is Partially Reversed by miR-27b-3p

Background

lncRNA HCP5 plays a cancer-promoting role in a variety of cancers. This study was the first to explore the mechanism of HCP5 in gastric carcinoma (GC).

Material/Methods

The differences in HCP5 between GC patients and healthy people were revealed in the TCGA database. The expression of HCP5 in GC tissues and adjacent tissues was compared by qRT-PCR. At the same time, the clinic pathological features of the patients were counted. Starbase and luciferase assay predicted and verified that miR-27b-3p is a targeted miRNA for HCP5. The expression of HCP5 and miR-27b-3p in various GC cells was detected by qRT-PCR. Cell viability and metastasis in different treatment groups were assessed by use of Cell Couting Kit-8 assay and clone formation assay, wound-healing assay, and transwell assay. Finally, expression of epithelial-mesenchymal transition (EMT)-associated markers was detected by Western blot.

Results

We found that HCP5 was overexpressed in GC tissues. Patients with higher expression of HCP5 had larger tumors, were more likely to have lymph node metastasis, and had higher TNM stage. HCP5 was overexpressed in GC cells, but this was reversed by miR-27b-3p. Silencing HCP5 inhibited GC cell viability and metastasis by downregulating Vimentin and N-cadherin and up-regulating E-cadherin, but this effect was partially reversed by miR-27b-3p inhibitor.

Conclusions

The effect of silencing HCP5 on repressing GC cells viability and metastasis by regulating EMT-associated markers can be partially reversed by miR-27b-3p inhibitor.

CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.

ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer

BACKGROUND

Gastric cancer (GC) is among the most common forms of cancer affecting the digestive system. This study sought to identify hub genes regulating early GC (EGC) in order to explore their potential for early diagnosis and prognosis of patients.

METHODS

We utilized a publically available dataset from the Gene Expression Omnibus database (GSE55696). Differences between EGC and LGIN with respect to gene expression were compared using the limma software. Identified differentially expressed genes (DEGs) were subjected to gene ontology (GO) and pathway enrichment analyses with the DAVID application, and the STRING website and Cytoscape software were used to construct a protein-protein interaction (PPI) network incorporating these DEGs. This network was in turn used to identify hub genes among selected DEGs, which were analyzed with the Kaplan-Meier Plotter database. In addition, Western blotting, qRT-PCR, immunohistochemistry, and UALCAN were all employed to validate the relationship between the expression of these genes and GC patient prognosis.

RESULTS

A total of 482 DEGs were identified, with GO analyses indicating an increase in the expression of genes linked with the development of cancer. Pathway analyses also indicated that these genes play a role in certain cancer-related pathways. The PPI network highlighted four potential hub genes, of which only ICAM1 was linked to a poor GC patient prognosis. This link between ICAM1 and GC patient outcomes was confirmed via UALCAN, Western blotting, immunohistochemistry, and qRT-PCR.

CONCLUSION

ICAM1 may therefore modulate tumor progression in GC, thus potentially representing a valuable prognostic and diagnostic biomarker of EGC.

Biglycan as a potential diagnostic and prognostic biomarker in multiple human cancers

Biglycan (BGN), a key member of the small leucine-rich proteoglycan family, is an important component of the extracellular matrix. Clinical studies have demonstrated that upregulation of BGN is associated with poor prognosis in patients with various types of solid cancer. The present study analyzed the mRNA expression levels of BGN in various types of solid cancer when compared with that in normal tissues via the Oncomine database. The UALCAN, OncoLnc and Kaplan-Meier Plotter databases were additionally used to evaluate the prognostic values of BGN in patients with solid cancer and co-expression gene analysis was conducted using the protein-protein interaction networks of BGN. The present study observed that the mRNA expression levels of BGN were increased in bladder, brain and central nervous system, breast, colorectal, esophageal, gastric, head and neck, lung, ovarian and 28 subtypes of cancer compared with normal tissues. The increased expression of BGN was identified to be associated with a poor outcome in ovarian and gastric cancer. Based on the co-expression network, BGN was identified as the key gene in a 43-gene network. The present findings of increased expression of BGN in solid tumors and its positive association with poor outcome on patient survival indicate that BGN may serve as a prognostic marker and as a target for novel therapeutics for multiple types of cancer.

The circular RNA circ-ITCH acts as a tumour suppressor in osteosarcoma via regulating miR-22

Background: Osteosarcoma (OS) is the most prevailing primary bone tumour and the third prevalent tumour in children and adolescents. Despite advanced treatments, the survival rate of OS has not been effectively improved. Here, we intended to investigate the functional impacts of circ-ITCH on OS. Methods: Circ-ITCH expression in OS tissues and cells was evaluated utilizing qRT-PCR. Viability and proliferation of MG63 and Saos-2 cells were determined by utilizing CCK-8 assay and BrdU assay. Transwell assay was utilized to investigate migration and invasion. Western blot was utilized to distinguish apoptosis and metastasis-related proteins expression. Sequentially, the above-mentioned parameters were reassessed when up-regulating miR-22. Results: Circ-ITCH was low expressed in OS tissues and cells. Overexpressing circ-ITCH facilitated apoptosis and repressed viability, proliferation, migration and invasion in MG63 and Saos-2 cells. MiR-22 expression was reduced by overexpressing circ-ITCH. The decline of viability, proliferation, migration and invasion made by overexpressing circ-ITCH was alleviated by up-regulating miR-22. Conclusively, circ-ITCH suppressed PTEN/PI3K/AKT and SP-1 pathways via down-regulating miR-22. Conclusion: Circ-ITCH took effects on apoptosis, viability, proliferation, migration and invasion through restraining PTEN/PI3K/AKT and SP-1 pathways via down-regulating miR-22 in MG63 and Saos-2 cells. Highlights Low expression of circ-ITCH is observed in osteosarcoma tissues and cell lines; Overexpression circ-ITCH suppresses miR-22 expression; Circ-ITCH promotes proliferation and represses apoptosis by up-regulating miR-22; Circ-ITCH promotes migration and invasion by up-regulating miR-22; Circ-ITCH activates PTEN/PI3K/AKT and SP-1 pathways by up-regulating miR-22.

Silencing circZFR inhibits the proliferation, migration and invasion of human renal carcinoma cells by regulating miR-206

Background

Renal cell carcinoma (RCC) is the most prevalent kind of kidney cancer. At present, the most efficient treatment mean is surgery. 40% patients with clear cell RCC (ccRCC) relapse after surgery. Identifying novel therapeutic markers and spots for early detection and treatment of RCC is necessary.

Methods

qRT-PCR was utilized to quantify circZFR and miR-206 expression in CAKI-1 and ACHN cells. Cell viability was detected by CCK-8 assay. Colony formation capacity was measured by colony formation assay. Transwell assay was utilized to investigate migration and invasion capacity. Expression of migration and apoptosis-associated proteins was quantified by Western blot.

Results

As a result, circZFR was highly expressed in RCC tissues and cells. Si-circZFR suppressed cell growth, migration and invasion of experimental cells. In addition, knockdown of circZFR upregulated miR-206 expression. Moreover, the antigrowth, antimigrating and anti-invasive effects of si-circZFR were attenuated when downregulating miR-206. Furthermore, Met is the target gene of miR-206 in experimental cells. The suppression on these signaling pathways was acted by targeting miR-206/Met axis.

Conclusion

The results demonstrated si-circZFR inhibited cell growth, migration and invasion in experimental cells by up-regulating of miR-206. Furthermore, si-circZFR suppressed Wnt/β-catenin and PI3K/AKT pathways via targeting miR-206/Met axis.