Role of Sp1 expression in gastric cancer: A meta-analysis and bioinformatics analysis

Circ-0000979 promotes the development of gastric carcinoma by sponging miR-136 and modulating SP1 mRNA expression level

Circular RNAs (circRNAs) are a new class of non-coding RNAs that play pivotal biological roles in several types of cancer cells. However, the role of circ-0000979 in gastric cancer (GC) has never been explored. Therefore, the current study aims to examine the functional effects of circ-0000979 in GC development and progression. The expression level of circ-0000979 was validated using qRT-PCR analysis. We found that circ-0000979 is significantly upregulated in GC samples. Using AGS and HGC27 GC cell line, we examined the biological functions and regulatory mechanisms of circ-0000979 in GC in vitro and in vivo by knocking down circ-0000979. We found that circ-0000979 is sub-cellularly localized in the cytoplasm of GC cells. Functionally, silencing circ-0000979 leads to a significant reduction in GC cell proliferation and migration. In vivo assays showed that circ-0000979 knockdown markedly reduced GC tumor growth. CircRNA interactome predicted miR-136 as circ-0000979 targeting miRNA, while starbase prediction result showed that miR-136 targeted the 3'UTR region of SP1 mRNA. Taken together, our results demonstrated that circ-0000979, as a carcinogenic circRNA, promotes the progression of GC by regulating the miR-136/SP1 pathway. Circ-0000979 is a potential RNA-based therapeutic target for GC treatment.

Low SP1 SUMOylation-dependent SNHG17 upregulation promotes drug resistance of gastric cancer through impairing hsa-miR-23b-3p-induced Notch2 inhibition

OBJECTIVE

Specificity protein 1 (SP1), a transcription factor mediated by SUMOylation modifiers, is upregulated in gastric cancer (GC) and shares negative correlation with patient prognosis. Here, we paid main attention to the role of SP1 SUMOylation in the drug resistance of GC cells and the possible long non-coding RNA (lncRNA) SNHG17/microRNA-23b-3p (miR-23b-3p)/Notch2 network engaged in this process.

METHODS

Tumor tissues and non-tumor tissues were isolated from GC patients who received treatment with capecitabine and cisplatin (DDP). Co-immunoprecipitation was utilized to detect the SUMOylation level of SP1. Using gain- and loss-of-function approaches, we assessed the impacts of SNHG17/miR-23b-3p/Notch2 on sensitivity of DDP-resistant GC cells in vitro and in vivo. A series of assays such as luciferase activity detection and RNA pull-down were conducted for mechanistic exploration.

RESULTS

SP1 expression was increased due to low SP1 SUMOylation level in the recurrent GC tissues. This increase led to upregulated SNHG17 expression and SP1 binding sites existed in the SNHG17 promoter. In addition, SNHG17 could bind to miR-23b-3p while miR-23b-3p targeted Notch2. Loss of SNHG17 reduced the resistance of DDP-resistant GC cells to DDP, which was achieved through miR-23b-3p-dependent Notch2 inhibition. Finally, SP1 silencing attenuated the resistance of GC to DDP in mice.

CONCLUSION

Low SP1 SUMOylation induces SNHG17 upregulation and blocks miR-23b-3p-induced Notch2 inhibition, contributing to the resistance of GC to DDP. This study may aid in the development of therapeutic targets overcoming the chemoresistance of GC.

Helicobacter pylori CagA Protein Regulating the Biological Characteristics of Gastric Cancer through the miR-155-5p/SMAD2/SP1 axis

Helicobacter pylori (Hp) is a grade Ι carcinogen of gastric cancer (GC), and its high infection rate seriously affects human health. Cytotoxin-associated gene A (CagA) plays a key role in the carcinogenesis of Hp as one of its main virulence factors. miR-155-5p is abnormally expressed in patients with GC, associated with the occurrence and development of cancer. However, little is known about the association between CagA and miR-155-5p. (1) Background: This study explored the association and mechanism of CagA and miR-155-5p in GC. (2) Methods: The CagA sequence was obtained from the NCBI. After sequence optimization, it was connected to the pcDNA3.1 vector to construct a CagA eukaryotic expression plasmid (pcDNA-CagA). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to investigate the expression of miR-155-5p and CagA in GC cells. The function of CagA on GC cells was detected by CCK8, wound healing, and Transwell assays. Similarly, the function of miR-155-5p was also studied through the above functional experiments after the miR-155-5p overexpression and knockdown models had successfully been constructed. The associations among CagA, miR-155-5p, and SMAD2/SP1 were evaluated using RNA immunoprecipitation (RIP) and rescue experiments. (3) Results: The expression of miR-155-5p was significantly reduced in GC cells, and the expression of miR-155-5p was further reduced after CagA induction. Both overexpressed CagA and knockdown miR-155-5p cell models enhanced malignant transformation, whereas overexpressed miR-155-5p inhibited malignant transformation in vitro. The function of miR-155-5p on GC cells could be influenced by CagA. We also found that the influence of miR-155-5p on SMAD2 and SP1 could be regulated by CagA. (4) Conclusions: CagA potentially regulates the biological function of GC cells through the miR-155-5p/SMAD2/SP1 axis. miR-155-5p could be a therapeutic target for GC related to CagA.

RHOA protein expression correlates with clinical features in gastric cancer: a systematic review and meta-analysis

Background

Gastric cancer (GC) is one of the most fatal cancers worldwide and is generally only detected after it has progressed to an advanced stage. Since there is a lack of comprehensive data on RHOA protein expression of patients with GC, this study utilized a systematic review and meta-analysis to address the limitation. The objective of this meta-analysis was to link GC clinical features with RHOA protein high- vs. low-expressing patients with GC.

Methods

The PubMed and Web of Science were used for a systematic literature review of GC related to RHOA. The included studies were obtained from two literature databases from past to Aug 31, 2021, by searching keywords. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The odds ratios (ORs) and 95% confidential intervals (CIs) for clinical features were estimated according to the high and low protein expression levels of RhoA. The mean effect sizes of ORs were obtained using the random-effects and fixed-effects models of meta-analysis. Heterogeneity of the studies was assesed by using statistics: τ², I²; and Q values. The symmetry of funnel plots were inspected for publication bias.

Results

Finally, 10 studies including 1,389 patients with GC (735 RHOA-positive and 654 RHOA-negative) were eligible for our meta-analysis to estimate associations between the protein expression and clinical features (e.g., Union for International Cancer Control [UICC] stage progression, differentiation, Lauren histological classification, and vascular invasion). In our meta-analysis, RHOA positive expression was determined to have a statistically significant association with UICC stage progression (P = 0.02) and poorly differentiated status (P = 0.02). The association between RHOA positivity and Lauren subtypes was not statistically significant (P = 0.07).

Conclusions

This meta-analysis suggested that RhoA protein expression in patients with GC was associated with clinical features: UICC stage progression and poorly differentiated status. Our findings are inconclusive but indicate that high RHOA protein expressing patients with GC could predict advanced UICC stages. A large prospective cohort study is required for validation in future.

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

Background: Gastric cancer (GC) is one of the most prevalent cancers all over the world. The molecular mechanisms of GC remain unclear and not well understood. GC cases are majorly diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to get a better understanding of precise molecular mechanisms and enable us to identify the key genes in the carcinogenesis and progression of GC.

Methods: The present study used datasets from the GEO database to screen differentially expressed genes (DEGs) between GC and normal gastric tissues. GO and KEGG enrichments were utilized to analyze the function of DEGs. The STRING database and Cytoscape software were applied to generate protein–protein network and find hub genes. The expression levels of hub genes were evaluated using data from the TCGA database. Survival analysis was conducted to evaluate the prognostic value of hub genes. The GEPIA database was involved to correlate key gene expressions with the pathological stage. Also, ROC curves were constructed to assess the diagnostic value of key genes.

Results: A total of 607 DEGs were identified using three GEO datasets. GO analysis showed that the DEGs were mainly enriched in extracellular structure and matrix organization, collagen fibril organization, extracellular matrix (ECM), and integrin binding. KEGG enrichment was mainly enriched in protein digestion and absorption, ECM-receptor interaction, and focal adhesion. Fifteen genes were identified as hub genes, one of which was excluded for no significant expression between tumor and normal tissues. COL1A1, COL5A2, P4HA3, and SPARC showed high values in prognosis and diagnosis of GC.

Conclusion: We suggest COL1A1, COL5A2, P4HA3, and SPARC as biomarkers for the diagnosis and prognosis of GC.

Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease. Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships. Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events. Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

Sp1-Induced FNBP1 Drives Rigorous 3D Cell Motility in EMT-Type Gastric Cancer Cells

Cancer is heterogeneous among patients, requiring a thorough understanding of molecular subtypes and the establishment of therapeutic strategies based on its behavior. Gastric cancer (GC) is adenocarcinoma with marked heterogeneity leading to different prognoses. As an effort, we previously identified a stem-like subtype, which is prone to metastasis, with the worst prognosis. Here, we propose FNBP1 as a key to high-level cell motility, present only in aggressive GC cells. FNBP1 is also up-regulated in both the GS subtype from the TCGA project and the EMT subtype from the ACRG study, which include high portions of diffuse histologic type. Ablation of FNBP1 in the EMT-type GC cell line brought changes in the cell periphery in transcriptomic analysis. Indeed, loss of FNBP1 resulted in the loss of invasive ability, especially in a three-dimensional culture system. Live imaging indicated active movement of actin in FNBP1-overexpressed cells cultured in an extracellular matrix dome. To find the transcription factor which drives FNBP1 expression in an EMT-type GC cell line, the FNBP1 promoter region and DNA binding motifs were analyzed. Interestingly, the Sp1 motif was abundant in the promoter, and pharmacological inhibition and knockdown of Sp1 down-regulated FNBP1 promoter activity and the transcription level, respectively. Taken together, our results propose Sp1-driven FNBP1 as a key molecule explaining aggressiveness in EMT-type GC cells.

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Bioinformatics analysis of the expression of HOXC13 and its role in the prognosis of breast cancer

The homeobox (HOX) genes, a class of transcription factors, are known to promote embryonic development and induce tumor formation. To date, the HOXA and HOXB gene families have been reported to be associated with breast cancer. However, the expression and exact role of homeobox C13 (HOXC13) in breast cancer has not yet been investigated. In the present study, the HOXC13 expression in human breast cancer was evaluated using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). Next, the Gene expression-based Outcome for Breast cancer online database, cBioportal, University of California Santa Cruz Xena browser and bc-GenExMinerv were used to explore the specific expression of HOXC13 in breast cancer. The methylation and mutation status of HOXC13 in breast cancer was then validated using the CCLE and cBioportal databases. Finally, the co-expression of HOX transcript antisense RNA (HOTAIR) and HOXC13 in breast cancer were analyzed and their impact on clinical prognosis determined. It was found that the expression of HOXC13 was high in breast cancer compared with other types of cancer, such as gastric cancer and colon cancer. Following co-expression analysis, a significant positive association was identified between HOTAIR and HOXC13. An association between HOTAIR and HOXC13, and lymph node and distant metastasis recurrence was also revealed during the development of breast cancer. Of note, survival analysis showed that high expression of HOTAIR and HOXC13 predicted poor prognosis. These findings revealed that HOXC13 plays an important role in the progression of breast cancer. However, the specific mechanism needs to be confirmed by subsequent experiments.