Differentially expressed genes ASPN, COL1A1, FN1, VCAN and MUC5AC are potential prognostic biomarkers for gastric cancer

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.

Identification and validation of key genes associated with pathogenesis and prognosis of gastric cancer

Background

Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. However, the precise mechanisms and specific biomarkers of GC have not been fully elucidated. We therefore sought to identify and validate the genes associated with GC.

Methods

RNA sequencing was performed on gastric tissue specimens from 10 cases each of non-atrophic gastritis (NAG), intestinal metaplasia (IM), and GC. Validation of gene expression was conducted through immunohistochemistry (IHC) staining. The Kaplan-Meier Plotter database was utilized to screen genes associated with prognosis, while protein-protein interaction analysis was conducted to identify hub genes.

Results

In GC-IM, the differentially expressed genes (DEGs) were predominantly enriched in pathways related to ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, and pathways in cancer. Conversely, in IM-NAG, the DEGs were primarily enriched in pathways associated with fat digestion and absorption, pancreatic secretion, and retinol metabolism. IHC staining revealed elevated expression levels of KLK7 and KLK10 in GC. Specifically, KLK7 expression was found to be correlated with differentiation (P = 0.025) and depth of invasion (P = 0.007) in GC, while both KLK7 and KLK10 were associated with the overall survival (P < 0.05). Furthermore, a total of ten hub genes from DEGs in GC-NAG (COL6A2, COL1A1, COL4A1, COL1A2, SPARC, COL4A2, FN1, PCOLCE, SERPINH1, LAMB1) and five hub genes in IM-NAG (SI, DPP4, CLCA1, MEP1A, OLFM4) were demonstrated to have a significant correlation with the prognosis of GC.

Conclusions

The present study successfully identified and validated crucial genes associated with GC, providing valuable insights into the underlying mechanisms of this disease. The findings of this study have the potential to inform clinical practice.

Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common malignant tumors with poor prognosis in terms of advanced stage. However, the survival-associated biomarkers for GC remains unclear.

AIM

To investigate the potential biomarkers of the prognosis of patients with GC, so as to provide new methods and strategies for the treatment of GC.

METHODS

RNA sequencing data from The Cancer Genome Atlas (TCGA) database of STAD tumors, and microarray data from Gene Expression Omnibus (GEO) database (GSE19826, GSE79973 and GSE29998) were obtained. The differentially expressed genes (DEGs) between GC patients and health people were picked out using R software (x64 4.1.3). The intersections were underwent between the above obtained co-expression of differential genes (co-DEGs) and the DEGs of GC from Gene Expression Profiling Interactive Analysis database, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we verified the candidate Hub genes.

RESULTS

With DEGs analysis, there were 334 co-DEGs, including 133 up-regulated genes and 201 down-regulated genes. GO enrichment analysis showed that the co-DEGs were involved in biological process, cell composition and molecular function pathways. KEGG enrichment analysis suggested the co-DEGs pathways were mainly enriched in ECM-receptor interaction, protein digestion and absorption pathways, etc. GSEA pathway analysis showed that co-DEGs mainly concentrated in cell cycle progression, mitotic cell cycle and cell cycle pathways, etc. PPI analysis showed 84 nodes and 654 edges for the co-DEGs. The survival analysis illustrated 11 Hub genes with notable significance for prognosis of patients were screened. Furtherly, using IHC database of HPA, we confirmed the above candidate Hub genes, and 10 Hub genes that associated with prognosis of GC were identified, namely BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN.

CONCLUSION

The 10 Hub genes may be the potential biomarkers for predicting the prognosis of GC, which can provide new strategies and methods for the diagnosis and treatment of GC.

Joint effect of THBS2 and VCAN accelerating the poor prognosis of gastric cancer

OBJECTIVE

Gastric cancer is the most common malignant tumor of the digestive system. The progression from gastritis to gastric cancer may be related to genetic factors, but the specific molecular mechanism remains unclear. Therefore, an in-depth study of the molecular mechanism of gastritis and gastric cancer is significant.

METHODS

We downloaded two gene profiles, GSE2669 and GSE116312, from the Gene Expression Omnibus (GEO) database. This study aims to apply bioinformatics technology to mine differentially expressed genes (DEGs), DEGs annotation, protein-protein interaction (PPI) network creation, and hub gene identification and expression between gastric cancer patients and gastritis patients. Overall survival analysis of hub genes, analysis by comparative toxicogenomics database for hub genes in gastric cancer, THBS2 and VCAN protein expression by immunohistochemistry for gastric cancer and gastritis as well as design of the biological process (BP) neural network was implemented.

RESULTS

The MSLN, SPP1, THBS2, SPARC, FN1, IGFBP7, VCAN were up-regulated in gastric carcinoma samples, while FGA was down-regulated. The protein expression of THBS2 and VCAN in gastric cancer was significantly higher than that in gastritis. VCAN protein expression was positively associated with tumor invasion (P = 0.011) and HER2 overexpression (P = 0.031). Strong correlation among THBS2, VCAN, and gastric cancer based on the BP neural network.

CONCLUSION

THBS2 and VCAN may be potential targets for improving gastric cancer patients' diagnosis and clinical efficacy.

Fibronectin 1 as a Key Gene in the Genesis and Progression of Cadmium-Related Bladder Cancer

Exposure to cadmium (Cd), a non-essential heavy metal, leads to the malignant transformation of urothelial cells and promotes bladder cancer (BC) development, but the mechanisms are unclear. Therefore, we aimed to explore the possible molecules associated with Cd-related BC. By analyzing and mining biological big data in public databases, we screened genes associated with the malignant transformation of uroepithelial cells caused by Cd and further screened the key gene associated with BC prognosis from these genes. The possible roles of the key gene in BC progression were then further explored through biological big data analysis and cellular experiments. Data mining yielded a total of 387 malignant transformation-related genes, which were enriched in multiple cancer-related signaling pathways, such as cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, and Jak-STAT signaling pathway. Further screening identified Fibronectin 1 (FN1) as the key gene. High expression of FN1 was associated with the advanced pathologic stage, T stage, N stage, and M stage and predicted an unfavorable outcome in BC patients. FN1 expression was positively associated with the infiltration of several types of immune cells, particularly tumor-associated macrophages and cancer-associated fibroblasts. Overexpression of FN1 could be detected in Cd-treated urothelial cells and BC cell lines. Interestingly, silencing of FN1 impaired the proliferation and invasive capacity of BC cells. In conclusion, the present study provides new insight into the mechanism of Cd-related BC. FN1 might be a prognostic marker and therapeutic target for BC. Future studies are needed to confirm these results.

The Regulatory Network of Gastric Cancer Pathogenesis and Its Potential Therapeutic Active Ingredients of Traditional Chinese Medicine Based on Bioinformatics, Molecular Docking, and Molecular Dynamics Simulation

Objective. This study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored. Methods. The Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking. Results. A total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding. Conclusion. Our findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

Identification of Glucose Metabolism-Related Genes in the Progression from Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma

Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma.

LncRNA LINC00511 promotes COL1A1-mediated proliferation and metastasis by sponging miR-126-5p/miR-218-5p in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is currently the leading cause of cancer-related death worldwide. Long noncoding RNAs (lncRNAs) play key roles in tumor occurrence and development as crucial cancer regulators. The present study aimed to explore the molecular mechanism and regulatory network of Linc00511 in LUAD and to identify new potential therapeutic targets for LUAD.

Methods

Real-time quantitative polymerase chain reaction (RT–qPCR) was performed to determine the relative Linc00511 levels in LUAD tissues and cells. The proliferation, apoptosis, migration, and invasion abilities of LUAD cells were assessed by a Cell Counting Kit-8 (CCK-8) assay, a colony formation assay, flow cytometry, and a Transwell assay. Changes in hsa_miR-126-5p, hsa_miR-218-5p, and COL1A1 expression were analyzed using western blotting and RT–qPCR. Targeted binding between miR-126-5p/miR-218-5p and Linc00511 or COL1A1 was verified with a luciferase reporter system and confirmed by an RNA pulldown assay. The participation of the PI3K/AKT signaling pathway was confirmed via western blotting. Xenograft animal experiments were performed to detect the impact of Linc00511 on LUAD tumor growth in vivo.

Results

In the present work, we observed that Linc00511 was upregulated in LUAD tissues and cells. Loss/gain-of-function experiments indicated that knockdown of Linc00511 significantly inhibited LUAD cell proliferation, migration and invasion and promoted LUAD cell apoptosis, whereas overexpression of Linc00511 showed the opposite effects. In addition, we determined that Linc00511 promoted COL1A1-mediated cell proliferation and cell motility by sponging miR-126-5p and miR-218-5p. Moreover, Linc00511 activated the PI3K/AKT signaling pathway through upregulation of COL1A1. Finally, silencing of Linc00511 inhibited LUAD tumor growth in vivo.

Conclusions

Linc00511 acts as a competing endogenous RNA to regulate COL1A1 by targeting miR-126-5p and miR-218-5p, thereby promoting the proliferation and invasion of LUAD cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02070-3.

Discovery of Long Non-Coding RNA MALAT1 Amplification in Precancerous Colorectal Lesions

A colorectal adenoma, an aberrantly growing tissue, arises from the intestinal epithelium and is considered as precursor of colorectal cancer (CRC). In this study, we investigated structural and numerical chromosomal aberrations in adenomas, hypothesizing that chromosomal instability (CIN) occurs early in adenomas. We applied array comparative genomic hybridization (aCGH) to fresh frozen colorectal adenomas and their adjacent mucosa from 16 patients who underwent colonoscopy examination. In our study, histologically similar colorectal adenomas showed wide variability in chromosomal instability. Based on the obtained results, we further stratified patients into four distinct groups. The first group showed the gain of MALAT1 and TALAM1, long non-coding RNAs (lncRNAs). The second group involved patients with numerous microdeletions. The third group consisted of patients with a disrupted karyotype. The fourth group of patients did not show any CIN in adenomas. Overall, we identified frequent losses in genes, such as TSC2, COL1A1, NOTCH1, MIR4673, and GNAS, and gene gain containing MALAT1 and TALAM1. Since long non-coding RNA MALAT1 is associated with cancer cell metastasis and migration, its gene amplification represents an important event for adenoma development.

RNA Sequencing of Tumor-Educated Platelets Reveals a Three-Gene Diagnostic Signature in Esophageal Squamous Cell Carcinoma

There is no cost-effective, accurate, and non-invasive method for the detection of esophageal squamous cell carcinoma (ESCC) in clinical practice. We aimed to investigate the diagnostic potential of tumor-educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into a training cohort (23 patients and 27 healthy individuals) and a validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants, and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2, and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor-educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found that keratin and collagen protein families and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics.

Hsa_circ_0000285 contributes to gastric cancer progression by upregulating FN1 through the inhibition of miR-1278

Background

Gastric cancer (GC) is one of the most severe cancers worldwide, particularly in China. Circular RNA (circRNA) plays an essential role in GC. Hsa_circ_0000285 regulates the progression of several cancers. However, its role in GC has not been reported. This study elucidated the molecular mechanism and role of hsa_circ_0000285 in GC progression.

Methods

GC cells were transfected with silencers of hsa_circ_0000285 and fibronectin 1 (FN1), an inhibitor of miR‐1278, and their negative controls (NC). Mice were injected with short hairpin (sh) RNAs targeting hsa_circ_0000285 or NC. The expression levels of hsa_circ_0000285, miR‐1278, and FN1 were assessed using western blotting and reverse transcription quantitative real‐time polymerase chain reaction (qRT‐PCR). Several assays were used to evaluate cell proliferation, invasion, and apoptosis. Tumor burden was also analyzed. The interactions between miR‐1278, hsa_circ_0000285, and FN1 were ascertained using dual‐luciferase reporter assays. An RNA immunoprecipitation (RIP) assay was used to assess the enrichment of hsa_circ_0000285 and miR‐1278 in GC.

Results

Hsa_circ_0000285 was significantly overexpressed in the GC tissues. Silencing hsa_circ_0000285 inhibited cell proliferation and invasion, promoted apoptosis, and inhibited tumor development. Hsa_circ_0000285 sponged miR‐1278. Inhibition of miR‐1278 in vitro reversed the effects of hsa_circ_0000285 silencing on GC progression. MiR‐1278 targeted FN1, and silencing FN1 neutralized the effects of miR‐1278 inhibitors on GC progression.

Conclusions

The hsa_circ_0000285/miR‐1278/FN1 axis regulated GC progression. In addition, it may serve as a potential therapeutic biomarker for GC.

Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis

This study was aimed at exploring common oncogenic genes and pathways both in osteosarcoma and Ewing's sarcoma. Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing's sarcoma tissues by RT-qPCR and western blot compared to normal tissues. 201 genes were differentially expressed. There were 121 nodes and 232 edges of the PPI network. Among 12 hub genes, hub genes FN1, COL1A1, and COL1A2 may involve in the development of osteosarcoma and Ewing's sarcoma. And they were reduced to expression both in osteosarcoma and Ewing's sarcoma tissues at mRNA and protein levels compared to normal tissues. Knockdown of FN1, COL1A1, and COL1A2 enhanced the cell proliferation and migration of U2OS under the restriction of cisplatin. Our findings revealed the common oncogenic genes such as FN1, COL1A1, and COL1A2, which may act as antioncogene by enhancing cisplatin sensitivity in osteosarcoma cells, and pathways were both in osteosarcoma and Ewing's sarcoma.

The Overexpression of Fibronectin 1 Promotes Cancer Progression and Associated with M2 Macrophages Polarization in Head and Neck Squamous Cell Carcinoma Patients

Purpose

This study aimed to investigate the biological roles of fibronectin 1 (FN1) in head and neck squamous cell carcinoma (HNSCC) and its effects on macrophage M2 polarization.

Methods

We analyzed FN1 expression pattern and examined its clinical relevance in HNSCC progression by bioinformatic analysis. Small interfering RNA (siRNA) was utilized to silence FN1 in HNSCC cells. Cell counting kit-8 (CCK-8) assay, colony formation assay, Transwell assay and wound healing assay were performed to reveal the effect of FN1 on malignant behaviors of HNSCC cells. Moreover, a co-culture model of macrophages and HNSCC cells was established to investigate whether FN1 induce macrophage M2 polarization. Finally, we used bioinformatic methods to explore the possible FN1-related pathways in HNSCC.

Results

FN1 is significantly overexpressed in HNSCC patients and has been obviously correlated with higher pathological stage and poor prognosis. Downregulation of FN1 suppressed the proliferation, migration and invasion of HNSCC cells, and inhibited macrophage M2 polarization in vitro. In addition, “PI3K-Akt” and “MAPK” signaling pathways may be involved in the malignant process of FN1 in HNSCC.

Conclusion

The overexpression of FN1 promotes HNSCC progression and induces macrophages M2 polarization. FN1 may serve as a promising prognostic biomarker and therapeutic target in HNSCC.

Drug metabolism-related eight-gene signature can predict the prognosis of gastric adenocarcinoma

Background

Metabolic abnormalities in patients with gastric adenocarcinoma lead to drug resistance and poor prognosis. Therefore, this study aimed to explore biomarkers that can predict the prognostic risk of gastric adenocarcinoma by analyzing drug metabolism‐related genes.

Methods

The RNA‐seq and clinical information on gastric adenocarcinoma were downloaded from the UCSC and gene expression omnibus databases. Univariate and least absolute shrinkage and selection operator regression analyses were used to identify the prognostic gene signature of gastric adenocarcinoma. The relationships between gastric adenocarcinoma prognostic risk and tumor microenvironment were assessed using CIBERSORT, EPIC, QUANTISEQ, MCPCounter, xCell, and TIMER algorithms. The potential drugs that could target the gene signatures were predicted in WebGestalt, and molecular docking analysis verified their binding stabilities.

Results

Combined with clinical information, an eight‐gene signature, including GPX3, ABCA1, NNMT, NOS3, SLCO4A1, ADH4, DHRS7, and TAP1, was identified from the drug metabolism‐related gene set. Based on their expressions, risk scores were calculated, and patients were divided into high‐ and low‐risk groups, which had significant differences in survival status and immune infiltrations. Risk group was also identified as an independent prognostic factor of gastric adenocarcinoma, and the established prognostic and nomogram models exhibited excellent capacities for predicting prognosis. Finally, miconazole and niacin were predicted as potential therapeutic drugs for gastric adenocarcinoma that bond stably with NOS3 and NNMT through hydrogen interactions.

Conclusions

This study proposed a drug metabolism‐related eight‐gene signature as a potential biomarker to predict the gastric adenocarcinoma prognosis risks.

Collagen type 1 alpha 1 chain is a novel predictive biomarker of poor progression-free survival and chemoresistance in metastatic lung cancer

Background: Collagen type 1 alpha 1 chain (COL1A1) is an extracellular matrix protein comprising two alpha 1 chains and one alpha 2 chain. Our previous study identified that COL1A1 is the key gene during the development and progression of lung adenocarcinoma by multi-omics analysis. However, the clinical significance of COL1A1 expression in lung cancer samples remains largely unknown. Here, we aimed to evaluate the level of COL1A1 in lung cancer samples and correlate its level with the clinical outcome.

Methods:COL1A1 gene expression in lung cancer samples was analyzed using the Oncomine database (www.oncomine.org). A total of 308 lung cancer samples (208 formalin-fixed paraffin-embedded tissues and 100 blood samples) were assessed for protein expression of COL1A1. Immunohistochemistry staining and enzyme-linked immunosorbent assay were used to detect COL1A1 expression in tissues and serum, respectively.

Results: We identified an elevation of COL1A1 in mRNA level and gene amplification in lung cancer tissues compared with normal lung tissues. High COL1A1 expression was observed in lung cancer tissues and serum (P < 0.05), it was significantly correlated with the peripheral type tumor, the larger diameter of the tumor, the occurrence of lymph node metastases and distant metastases, a higher TNM stage, and smoking (P < 0.05). High COL1A1 expression was associated with poor progression-free survival (PFS) and chemoresistance in lung cancer patients (P < 0.05). Multivariable Cox-regression analysis showed that COL1A1 expression was an independent prognostic factor (P < 0.05). Furthermore, the area under the receiver operating characteristic (AUC) curve was 0.909 for the combined COL1A1 and carcinoembryonic antigen (CEA) measurement.

Conclusion: Our findings revealed that COL1A1 could be used as a novel diagnostic, prognostic, and chemoresistance biomarker of human lung cancer, and these results provide a potential therapeutic strategy for lung cancer patients.

Reciprocal interplay between asporin and decorin: Implications in gastric cancer prognosis

Effective patient prognosis necessitates identification of novel tumor promoting drivers of gastric cancer (GC) which contribute to worsened conditions by analysing TCGA-gastric adenocarcinoma dataset. Small leucine-rich proteoglycans, asporin (ASPN) and decorin (DCN), play overlapping roles in development and diseases; however, the mechanisms underlying their interplay remain elusive. Here, we investigated the complex interplay of asporin, decorin and their interaction with TGFβ in GC tumor and corresponding normal tissues. The mRNA levels, protein expressions and cellular localizations of ASPN and DCN were analyzed using real-time PCR, western blot and immunohistochemistry, respectively. The protein-protein interaction was predicted by in-silico interaction analysis and validated by co-immunoprecipitation assay. The correlations between ASPN and EMT proteins, VEGF and collagen were achieved using western blot analysis. A significant increase in expression of ASPN in tumor tissue vs. normal tissue was observed in both TCGA and our patient cohort. DCN, an effective inhibitor of the TGFβ pathway, was negatively correlated with stages of GC. Co-immunoprecipitation demonstrated that DCN binds with TGFβ, in normal gastric epithelium, whereas in GC, ASPN preferentially binds TGFβ. Possible activation of the canonical TGFβ pathway by phosphorylation of SMAD2 in tumor tissues suggests its role as an intracellular tumor promoter. Furthermore, tissues expressing ASPN showed unregulated EMT signalling. Our study uncovers ASPN as a GC-promoting gene and DCN as tumor suppressor, suggesting that ASPN can act as a prognostic marker in GC. For the first time, we describe the physical interaction of TGFβ with ASPN in GC and DCN with TGFβ in GC and normal gastric epithelium respectively. This study suggests that prevention of ASPN-TGFβ interaction or overexpression of DCN could serve as promising therapeutic strategies for GC patients.

Prognostic signatures associated with high infiltration of Tregs in bone metastatic prostate cancer

Metastatic cancer especially bone metastasis (BM) is the lethal end-stage of castration-resistant prostate cancer (CRPC). To understand the possible molecular mechanisms underlying the development of the distant metastasis is of potential clinical value. We sought to identify differentially expressed genes between patient-matched primary and bone metastatic CRPC tumors. Functional enrichment, protein-protein interaction networks, and survival analysis of DEGs were performed. DEGs with a prognostic value considered as candidate genes were evaluated, followed by genetic analysis of tumor infiltrating immune cells based on Wilcoxon test and immunofluorescence identification. Expression profiles analysis showed that 381 overlapping genes were screened as differentially expressed genes (DEGs), of which 16 DEGs were randomly selected to be validated and revealed that most of these genes showed a transcriptional profile similar to that seen in the datasets (Pearson’s r = 0.76). Six core genes were found to be involved in regulation of extracellular matrix receptor interaction and chemotactic activity, and four of them were significantly correlated with the survival of PCa patients with bone metastases. Immune infiltration analysis showed that the expressions levels of COL3A1, RAC1, FN1, and SDC2 in CD4+T cells were significantly higher than those in tumor cells, especially regulatory T cell infiltration was significantly increased in BM tumors. We analyzed gene expression signatures specifically associated with the development of bone metastases of CRPC patients. Characterization of genes associated with BM of mCRPC is critical for identification of predictive biomarkers and potential therapeutic targets.

INHBA promotes the proliferation, migration and invasion of colon cancer cells through the upregulation of VCAN

OBJECTIVE

Colon cancer has high morbidity and mortality rates, and proliferation, invasion and migration play an important role in colon cancer progression. Here, the effects of inhibin subunit beta A (INHBA) on cell proliferation, invasion and migration were investigated.

METHODS

The UALCAN database was used to assess INHBA expression in colon cancer tissues and predict the survival of patients with high and low INHBA expression. The relevant proteins were detected by RT-qPCR and western blot. Cell transfection was performed to overexpress or inhibit INHBA and versican (VCAN). The high correlation between INHBA and VCAN found through LinkedOmics and StarBase databases was verified by immunoprecipitation assays. Cell proliferation was detected by cell counting kit-8 and colony formation assays. Wound healing and Transwell assays were used to assess migration and invasion.

RESULTS

INHBA expression was upregulated in colon cancer tissues and cells. INHBA inhibition impaired the proliferation, migration and invasion of these cells. In addition, we confirmed the correlation between INHBA and VCAN in colon cancer cells. Finally, we found that INHBA interference inhibited the aggressive behavior of colon cancer cells by downregulating VCAN.

CONCLUSION

INHBA promotes the proliferation, migration and invasion of colon cancer cells through the upregulation of VCAN.

A Robust Circular RNA-Associated Three-Gene Prognostic Signature for Patients with Gastric Cancer

Accumulating evidence has demonstrated that circular RNAs (circRNAs) play vital roles in cancer progression. However, the underlying molecular mechanisms of circRNAs remain poorly elucidated in gastric cancer (GC). The main purpose of present study is to explore the underlying regulatory mechanism by constructing a circRNA-associated competitive endogenous RNA (ceRNA) network and further establish a robust prognostic signature for patients with GC. Based on expression data of circRNA, microRNA, and mRNA derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, a circRNA-associated ceRNA network, containing 15 cirRNAs, 9 microRNAs, and 35 mRNAs, was constructed using the Starbase database. Functional enrichment analysis showed that the ceRNA network might be involved in many cancer-related pathways, such as regulation of transcription from RNA polymerase II promoter, mesodermal cell differentiation, and focal adhesion. A protein-protein interaction network was constructed based on genes within the circRNA-associated ceRNA network. We found that six of ten hub genes within the PPI network were significantly associated with overall survival (OS). Thus, using the LASSO method, we constructed a three-gene prognostic signature based on TCGA-GC cohort, which could classify GC patients into low-risk and high-risk groups with significant difference in OS (HR = 1.9, 95%CI = 1.14‐3.2, and log-rank p = 0.001). The prognostic performance of the three-gene signature was verified in GSE15459 (HR = 1.9, 95%CI = 1.27‐3.0, and log − rank p = 2.2E − 05) and GSE84437 (HR = 1.5, 95%CI = 1.17‐2.0, and log − rank p = 6.3E − 04). Multivariate Cox analysis further revealed that the three-gene prognostic signature could serve as an independent risk factor for OS. Taken together, our findings contribute to a better understanding of the underlying mechanisms of circRNAs in GC progression. Furthermore, a robust prognostic signature is meaningful to facilitate individualized treatment for patients with GC.

Prognostic and therapeutic implications of extracellular matrix associated gene signature in renal clear cell carcinoma

Complex interactions in tumor microenvironment between ECM (extra-cellular matrix) and cancer cell plays a central role in the generation of tumor supportive microenvironment. In this study, the expression of ECM-related genes was explored for prognostic and immunological implication in clear cell renal clear cell carcinoma (ccRCC). Out of 964 ECM genes, higher expression (z-score > 2) of 35 genes showed significant association with overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS). On comparison to normal tissue, 12 genes (NUDT1, SIGLEC1, LRP1, LOXL2, SERPINE1, PLOD3, ZP3, RARRES2, TGM2, COL3A1, ANXA4, and POSTN) showed elevated expression in kidney tumor (n = 523) compared to normal (n = 100). Further, Cox proportional hazard model was utilized to develop 12 genes ECM signature that showed significant association with overall survival in TCGA dataset (HR = 2.45; 95% CI [1.78-3.38]; p < 0.01). This gene signature was further validated in 3 independent datasets from GEO database. Kaplan-Meier log-rank test significantly associated patients with elevated expression of this gene signature with a higher risk of mortality. Further, differential gene expression analysis using DESeq2 and principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters between ECM-rich high-risk and ECM-poor low-risk patients. Geneset enrichment analysis (GSEA) identified significant perturbations in homeostatic kidney functions in the high-risk group. Further, higher infiltration of immunosuppressive T-reg and M2 macrophages was observed in high-risk group patients. The present study has identified a prognostic signature with associated tumor-promoting immune niche with clinical utility in ccRCC. Further exploration of ECM dynamics and validation of this gene signature can assist in design and application of novel therapeutic approaches.

An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer

Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.

COE Inhibits Vasculogenic Mimicry by Targeting EphA2 in Hepatocellular Carcinoma, a Research Based on Proteomics Analysis

New strategies and drugs are urgently needed to improve the treatment of hepatocellular carcinoma (HCC). Vasculogenic mimicry (VM) has been elucidated being associated with the progression of HCC and anti-VM could be a promising strategy. Celastrus orbiculatus extract (COE), a mixture of 26 compounds isolated from the Chinese Herb Celastrus Orbiculatus Vine, has been elucidated to be able to disrupt VM formation in HCC. This study aims to dissect and identify the potential targets of COE on anti-VM formation both in vitro and in vivo that are distinct from our previous study. Proteomics analysis was used to identify differential proteins in HCC cells treated with or without COE (Data are available via ProteomeXchange with identifier PXD022203). Cells invasion was examined using Transwell. Matrigel was used to establish a 3-D culture condition for VM formation in vitro. RT-PCR and Western Blot were used to examine changes of mRNA and protein respectively. Clinical resected samples were applied to confirm association between VM formation and identified targets. Subcutaneous xenograft tumor model was established to observe tumor growth and VM formation in vivo. PAS-CD34 dual staining was used to detect VM in vivo. A total of 194 proteins were identified to be differentially expressed in HCC cells treated with or without COE. In the 93 down-regulated proteins EphA2 stood out to be regulated on both RNA and protein level. Disruption EphA2 using COE or NVP inhibited VM formation and decreased VM associated biomarkers. In xenograft mouse model, COE inhibited tumor growth and VM formation via down-regulating EphA2. Taken together, our results indicate that COE could be used in HCC treatment because of its promising anti-VM effect.

TNRC6C Functions as a Tumor Suppressor and Is Frequently Downregulated in Papillary Thyroid Cancer

Our previous study found that trinucleotide repeat containing adaptor 6C (TNRC6C) may act as a tumor suppressor in papillary thyroid cancer (PTC). In this study, we aimed to confirm the effect of TNRC6C on PTC and investigate the underlying molecular mechanism. The difference of mRNA level of TNRC6C between PTC tissue and noncancerous thyroid tissue and the association of expression level of TNRC6C with clinicopathological features of PTC were analyzed using TCGA data. Immunohistochemical assay was performed to detect the protein expression of TNRC6C in PTC and its adjacent noncancerous tissue. Cell proliferation, migration, invasion, and apoptosis were analyzed after knockdown or overexpression of TNRC6C in BCPAP cells. RNA-sequencing was performed to find the target genes of TNRC6C, and potential targets were validated in BCPAP and TPC1 cells. Our results showed that TNRC6C was downregulated in PTC, and lower expression level of TNRC6C was associated with worse clinicopathological features. Overexpression of TNRC6C significantly inhibited proliferation, migration, and invasion of BCPAP cells and promoted its apoptosis, while knockdown of TNRC6C acted the opposite role. By analyzing RNA-sequencing data and TCGA data, 12 genes (SCD, CRLF1, APCDD1L, CTHRC1, PTPRU, ALDH1A3, VCAN, TNC, ECE1, COL1A1, CAMK2N2, and MMP14) were considered as potential target genes of TNRC6C, and most of them were associated with clinicopathological features of PTC in TCGA. All of them except CAMK2N2 were significantly downregulated after overexpressing TNRC6C. Our study demonstrated that TNRC6C functions as a tumor suppressor in PTC and may serve as a useful therapeutic target and prognostic marker for PTC patients.

Determinants and Functions of CAFs Secretome During Cancer Progression and Therapy

Multiple lines of evidence are indicating that cancer development and malignant progression are not exclusively epithelial cancer cell-autonomous processes but may also depend on crosstalk with the surrounding tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are abundantly represented in the TME and are continuously interacting with cancer cells. CAFs are regulating key mechanisms during progression to metastasis and response to treatment by enhancing cancer cells survival and aggressiveness. The latest advances in CAFs biology are pointing to CAFs-secreted factors as druggable targets and companion tools for cancer diagnosis and prognosis. Especially, extensive research conducted in the recent years has underscored the potential of several cytokines as actionable biomarkers that are currently evaluated in the clinical setting. In this review, we explore the current understanding of CAFs secretome determinants and functions to discuss their clinical implication in oncology.

Helicobacter pylori Infection and the Patterns of Gastric Mucin Expression in Children

Background: The updated model for the mechanism of gastric carcinogenesis demonstrates that Helicobacter pylori (H. pylori) is a risk factor in every step of the process. The expression of certain gastric mucins is altered by H. pylori infection in adult patients. The aim of our research was to assess the impact of H. pylori infection on the expression of secretory mucins in the pediatric antral mucosa. Methods: Slides were stained with monoclonal antibodies for MUC5AC, MUC6 and MUC2, digitalized and scored using both a semiquantitative and a quantitative approach. Results: The expression of MUC5AC was significantly lower in infected children. Also, MUC2 expression was more pronounced in infected children. MUC6 expression did not differentiate between infected and noninfected children. Additionally, the presence of chronic inflammation significantly altered the expression of MUC6 and MUC2. The expression of MUC6 was significantly higher in patients with gastric atrophy. Conclusion: The minor differences in mucin expression at distinct ages might stem from different H. pylori exposure periods. Further research is needed to determine the particular patterns of expression according to age and to evaluate the effects of the interaction between H. pylori and mucins in the progression of the gastric carcinogenesis cascade.

High expression of VCAN is an independent predictor of poor prognosis in gastric cancer

Objective

Methods

Results

Conclusion

Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

Background

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Methods

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Results

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Conclusion

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

Proteomic and transcriptomic studies of BGC823 cells stimulated with Helicobacter pylori isolates from gastric MALT lymphoma

BACKGROUND

The correlation between the infection of H. pylori and the occurrence of gastric MALT lymphoma (GML) has been well documented. However, the mechanism of how GML is caused by this bacterium is not well understood, although some immunologic mechanisms are thought to be involved.

MATERIALS AND METHODS

In this study, we performed both transcriptomic and proteomic analyses on gastric cancer cells infected by H. pylori isolates from GML patients and the gastric ulcer strain 26695 to investigate the differentially expressed molecular signatures that were induced by GML isolates.

RESULTS

Transcriptomic analyses revealed that the differentially expressed genes (DEGs) were mainly related to binding, catalytic activity, signal transducer activity, molecular transducer activity, nucleic acid binding transcription factor activity, and molecular function regulator. Fifteen pathways, including the Wnt signaling pathway, the mTOR signaling pathway, the NOD-like receptor signaling pathway and the Hippo signaling pathway, were revealed to be related to GML isolates. Proteomic analyses results showed that there were 116 differentially expressed proteins (DEPs). Most of these DEPs were associated with cancer, and 29 have been used as biomarkers for cancer diagnosis. We also found 63 upstream regulators that can inhibit or activate the expression of the DEPs. Combining the proteomic and transcriptomic analyses revealed 12 common pathways. This study provides novel insights into H. pylori-associated GML. The DEPs we found may be good candidates for GML diagnosis and treatment.

CONCLUSIONS

This study revealed specific pathways related to GML and potential biomarkers for GML diagnosis.

VUp-Regulation of VCAN Promotes the Proliferation, Invasion and Migration and Serves as a Biomarker in Gastric Cancer

Background

Versican (VCAN), a significant protein of extracellular matrix (ECM), is capable of accumulating in tumor stroma and critically impacts malignant transforming process and tumor progressing process. Promoted VCAN expression was identified in numerous malignant tumors and showed relationships to cancer relapse and ineffective breast, prostate, and many other cancer types of patients. Nevertheless, the molecular capability and prognosis importance exhibited by VCAN are infrequently presented in gastric cancer (GC).

Methods

According to 5 GC tissues and corresponding general tissues, mRNA expression profiles were taken here. VCAN expression in tissues was confirmed by quantitative reverse transcription polymerase reaction (qRT-PCR). The effect generated by VCAN expression on cell proliferating, invading and migrating processes was assessed in vitro with knockdown and overexpression strategies. Moreover, the relationships between immune response and VCAN expression in GC were assessed with the use of the software online.

Results

There are 181 genes up-regulated and 530 genes down-regulated in GC. According to pathway study, the mentioned differently expressed mRNAs showed correlations with a number of vital physiological processes, cellular components, molecular functions and critical cancer signal pathways. VCAN was reported to be noticeably promoted in GC tissues and related to individual cancer age, race, and stages. VCAN was up-regulated in 16 GC tissues compared to adjacent non-tumorous tissue specimens via qRT-PCR. GC patients exhibiting higher VCAN expression had less post-progression survival (PPS), first progression (FP) and overall survival (OS). Experimental processes in vitro revealed VCAN knockdown hindered, proliferated, invaded, and migrated levels of GC cells, whereas overexpression of VCAN played the opposite effect. Immune factors may interact with VCAN mRNA in GC, and VCAN was found noticeably linked with regulatory T cells (Tregs).

Conclusion

According to the mentioned results, VCAN critically impacts GC progression. Accordingly, VCAN is likely to be a potentially feasible prognosis marking element and a prominent cancer drug for GC patients.

Roles of Proteoglycans and Glycosaminoglycans in Cancer Development and Progression

The extracellular matrix (ECM) spatiotemporally controls cell fate; however, dysregulation of ECM remodeling can lead to tumorigenesis and cancer development by providing favorable conditions for tumor cells. Proteoglycans (PGs) and glycosaminoglycans (GAGs) are the major macromolecules composing ECM. They influence both cell behavior and matrix properties through direct and indirect interactions with various cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes, and glycoproteins within the ECM. The classical features of PGs/GAGs play well-known roles in cancer angiogenesis, proliferation, invasion, and metastasis. Several lines of evidence suggest that PGs/GAGs critically affect broader aspects in cancer initiation and the progression process, including regulation of cell metabolism, serving as a sensor of ECM's mechanical properties, affecting immune supervision, and participating in therapeutic resistance to various forms of treatment. These functions may be implemented through the characteristics of PGs/GAGs as molecular bridges linking ECM and cells in cell-specific and context-specific manners within the tumor microenvironment (TME). In this review, we intend to present a comprehensive illustration of the ways in which PGs/GAGs participate in and regulate several aspects of tumorigenesis; we put forward a perspective regarding their effects as biomarkers or targets for diagnoses and therapeutic interventions.

Mining TCGA database for genes of prognostic value in gastric cancer microenvironment

Gastric cancer (GC) is the sixth most common malignancy and the third leading cause of cancer‐related death worldwide. Emerging evidence suggests that tumour microenvironment cells play a vital role in the development and prognosis of GC. To investigate the possible effect of stromal scores and immune scores on the overall survival (OS) on the GC patients, we divided GC patients into ‘high’ and ‘low’ groups based on their stromal and immune scores, and found differentially expressed genes related to prognosis of GC patients. Functional enrichment analysis and GSVA further revealed that focal adhesion and ECM‐receptor interaction are associated with GC patients' survival. Finally, we analysed the effects of genes commonly involved in focal adhesion and ECM‐receptor interaction on GC patients' survival and validated our results in another GC cohort from GEO data sets. In conclusion, we obtained a list of tumour microenvironment‐related genes that predict poor prognosis in GC patients.

Identification of New Therapeutic Targets for Gastric Cancer With Bioinformatics

We aimed to identify new targets affecting gastric cancer (GC) prognosis. Six target genes were identified from hub genes based on their relationship with important factors affecting tumor progression, like immune infiltration, purity, tumor mutation burden (TMB), and tumor microenvironment (TME) score. The effect of target genes' somatic mutations and copy number alteration (CNA) was examined to determine their effect on GC prognosis. Six target genes (FBN1, FN1, HGF, MMP9, THBS1, and VCAN) were identified. Reduced expression of each target gene, except MMP9, indicated better prognosis and lower grade in GC. FBN1, THBS1, and VCAN showed lower expression in stage I GC. Non-silencing mutations of the six genes played a role in significantly higher TMB and TME scores. THBS1 mutation was associated with earlier stage GC, and VCAN mutation was associated with lower grade GC. However, patients with target gene CNA displayed higher tumor purity. MMP9, THBS1, and VCAN CNA was associated with lower grade GC, while FBN1 CNA reflected earlier T stage. Additionally, the target genes may affect GC prognosis by influencing multiple oncogenic signaling pathways. FBN1, FN1, HGF, MMP9, THBS1, and VCAN may be new GC prognostic targets by affecting tumor purity, TMB, TME score, and multiple oncogenic signaling pathways.

lncRNA TUG1 Promotes Cell Proliferation, Migration, and Invasion in Hepatocellular Carcinoma via Regulating miR-29c-3p/COL1A1 Axis

Background

Taurine upregulated gene 1 (TUG1) has been recognized as a novel oncogenic gene. The current study was established to explore the function and regulatory mechanism of TUG1 in hepatocellular carcinoma (HCC).

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression of TUG1, miR-29c-3p, and COL1A1 in tissues and cell lines. MTT assay, wound-healing and transwell assay were utilized for the detection of cell viability, migration and invasion, respectively. The interactions between miR-29c-3p and TUG1/COL1A1 were predicted by starBase v2.0 (http://starbase.sysu.edu.cn/) and verified by the dual-luciferase reporter or RNA immunoprecipitation assay. Western blot assay was performed to determine the protein levels of COL1A1, cyclin D1, E-cadherin, N-cadherin, Bcl-2, and Bax.

Results

Dramatically increased expression of TUG1 was noticed in HCC tissues and cell lines. TUG1 knockdown restrained the proliferation, migration, and invasion, and promoted the apoptosis of HCC cells. TUG1 targeted miR-29c-3p and inhibited miR-29c-3p expression. Overexpression of miR-29c-3p inhibited the proliferation, migration and invasion of HCC cells. MiR-29c-3p directly targeted COL1A1 and down-regulated COL1A1 expression. In addition, downregulation of miR-29c-3p and upregulation of COL1A1 both reversed the effects of TUG1 knockdown on the proliferation, apoptosis, migration, and invasion of HCC cells.

Conclusion

TUG1 could promote the proliferation, migration and invasion of HCC cells through regulating miR-29c-3p/COL1A1 axis. This novel finding might provide a latent target for the treatment of HCC.

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Background

Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA.

Methods

The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database.

Results

In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes.

Conclusion

The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

Identification of a Tumor Microenvironment-relevant Gene set-based Prognostic Signature and Related Therapy Targets in Gastric Cancer

Rationale: The prognosis of gastric cancer (GC) patients is poor, and there is limited therapeutic efficacy due to genetic heterogeneity and difficulty in early-stage screening. Here, we developed and validated an individualized gene set-based prognostic signature for gastric cancer (GPSGC) and further explored survival-related regulatory mechanisms as well as therapeutic targets in GC. Methods: By implementing machine learning, a prognostic model was established based on gastric cancer gene expression datasets from 1699 patients from five independent cohorts with reported full clinical annotations. Analysis of the tumor microenvironment, including stromal and immune subcomponents, cell types, panimmune gene sets, and immunomodulatory genes, was carried out in 834 GC patients from three independent cohorts to explore regulatory survival mechanisms and therapeutic targets related to the GPSGC. To prove the stability and reliability of the GPSGC model and therapeutic targets, multiplex fluorescent immunohistochemistry was conducted with tissue microarrays representing 186 GC patients. Based on multivariate Cox analysis, a nomogram that integrated the GPSGC and other clinical risk factors was constructed with two training cohorts and was verified by two validation cohorts. Results: Through machine learning, we obtained an optimal risk assessment model, the GPSGC, which showed higher accuracy in predicting survival than individual prognostic factors. The impact of the GPSGC score on poor survival of GC patients was probably correlated with the remodeling of stromal components in the tumor microenvironment. Specifically, TGFβ and angiogenesis-related gene sets were significantly associated with the GPSGC risk score and poor outcome. Immunomodulatory gene analysis combined with experimental verification further revealed that TGFβ1 and VEGFB may be developed as potential therapeutic targets of GC patients with poor prognosis according to the GPSGC. Furthermore, we developed a nomogram based on the GPSGC and other clinical variables to predict the 3-year and 5-year overall survival for GC patients, which showed improved prognostic accuracy than clinical characteristics only. Conclusion: As a tumor microenvironment-relevant gene set-based prognostic signature, the GPSGC model provides an effective approach to evaluate GC patient survival outcomes and may prolong overall survival by enabling the selection of individualized targeted therapy.

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

Gastric cancer (GC) is one of the most common malignant tumors in the world, and it is also the third largest cause of cancer-related death in the world. As far as we know, no biomarker has been widely accepted for early diagnosis and prognosis prediction of gastric cancer. The purpose of this study is to find potential biomarkers to predict the prognosis of GC. The gene expression profiles of GSE2685 were downloaded from GEO database. Morpheus was used to calculate the differentially expressed genes (DEGs) between primary advanced gastric cancer tissues and noncancerous gastric tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network of DEGs was constructed. Kaplan-Meier Plotter was used to determine the overall survival (OS) outcomes of UC5AC, MUC1, KRT7, GAPDH, CD44, and GEPIA was used to determine the Pearson correlation analysis. In total, 710 DEGs were identified in GC, including 396 upregulated genes and 314 downregulated genes. GO enrichment revealed that they were mainly enriched in binding, catalytic activity, cellular process and cell. KEGG pathway revealed that they were mainly enriched in metabolic pathways, pathways in cancer and PI3K-Akt signaling pathway. MUC5AC, MUC1, KRT7, GAPDH, CD44 were identified from the PPI network. MUC5AC, MUC1, KRT7, GAPDH, CD44 were demonstrated to have prognostic value for patients with GC. MUC5AC, MUC1 exhibited low expression levels in GC tissues, KRT7, GAPDH, CD44 presented high expression levels in GC tissues. In particular, KRT7 is hardly expressed in normal gastric tissues. MUC5AC and MUC1 were negatively correlated with GAPDH, CD44, respectively; and GAPDH was positively correlated with CD44 and KRT7, respectively. Moreover. MUC5AC, MUC1, KRT7, GAPDH, and CD44 are not only related to GC but also to apoptosis pathway. Results from the present study suggested that MUC5AC, MUC1, KRT7, GAPDH, CD44 may represent novel prognostic biomarkers for GC.

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.

Identification of potential key genes in gastric cancer using bioinformatics analysis

Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: ‘extracellular matrix organization’, ‘collagen catabolic process’ and ‘cell adhesion’. These three KEGG pathways were significantly enriched in the categories: ‘ECM-receptor interaction’, ‘protein digestion and absorption’, and ‘focal adhesion’. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Extracellular Matrix in the Tumor Microenvironment and Its Impact on Cancer Therapy

Solid tumors are complex organ-like structures that consist not only of tumor cells but also of vasculature, extracellular matrix (ECM), stromal, and immune cells. Often, this tumor microenvironment (TME) comprises the larger part of the overall tumor mass. Like the other components of the TME, the ECM in solid tumors differs significantly from that in normal organs. Intratumoral signaling, transport mechanisms, metabolisms, oxygenation, and immunogenicity are strongly affected if not controlled by the ECM. Exerting this regulatory control, the ECM does not only influence malignancy and growth of the tumor but also its response toward therapy. Understanding the particularities of the ECM in solid tumor is necessary to develop approaches to interfere with its negative effect. In this review, we will also highlight the current understanding of the physical, cellular, and molecular mechanisms by which the pathological tumor ECM affects the efficiency of radio-, chemo-, and immunotherapy. Finally, we will discuss the various strategies to target and modify the tumor ECM and how they could be utilized to improve response to therapy.

28-Hydroxy-3-oxoolean-12-en-29-oic Acid, a Triterpene Acid from Celastrus Orbiculatus Extract, Inhibits the Migration and Invasion of Human Gastric Cancer Cells In Vitro

Gastric cancer is the fifth most common tumor and has the third-highest mortality rate among various malignant tumors, and the survival rate of patients is low. Celastrus orbiculatus extract has been shown to inhibit the activity of a variety of tumors. This study explored the inhibitory effect of the oleanane-type triterpenoid acid 28-hydroxy-3-oxoolean-12-en-29-oic acid molecule from Celastrus orbiculatus extract on gastric cancer cell invasion and metastasis and determined its mechanism. 28-Hydroxy-3-oxoolean-12-en-29-oic acid was first diluted to various concentrations and then used to treat SGC-7901 and BGC-823 cells. Cell proliferation was assessed by an MTT (thiazole blue) assay. Transwell and wound healing assays were used to assess cell invasion and migration. High-content imaging technology was used to further observe the effects of the drug on cell invasion and migration. Western blotting was used to assess the effects on the expression of matrix metalloproteinases (MMPs) and the effects on epithelial-mesenchymal transition (EMT)-related proteins and phosphorylation-related proteins. We found that 28-Hydroxy-3-oxoolean-12-en-29-oic acid inhibited the migration and invasion of SGC-7901 and BGC-823 gastric cancer cells in a dose-dependent manner. Consequently, 28-hydroxy-3-oxoolean-12-en-29-oic acid decreased the expression of EMT-related proteins and MMPs in gastric cancer cells and reduced protein phosphorylation, inhibiting the migration and invasion of gastric cancer cells.

Multifaceted Roles of Asporin in Cancer: Current Understanding

The small leucine-rich proteoglycan (SLRP) family consists of 18 members categorized into five distinct classes, the traditional classes I–III, and the non-canonical classes IV–V. Unlike the other class I SLRPs (decorin and biglycan), asporin contains a unique and conserved stretch of aspartate (D) residues in its N terminus, and germline polymorphisms in the D-repeat-length are associated with osteoarthritis and prostate cancer progression. Since the first discovery of asporin in 2001, previous studies have focused mainly on its roles in bone and joint diseases, including osteoarthritis, intervertebral disc degeneration and periodontal ligament mineralization. Recently, asporin gene expression was also reported to be dysregulated in tumor tissues of different types of cancer, and to act as oncogene in pancreatic, colorectal, gastric, and prostate cancers, and some types of breast cancer, though it is also reported to function as a tumor suppressor gene in triple-negative breast cancer. Furthermore, asporin is also positively or negatively correlated with tumor proliferation, migration, invasion, and patient prognosis through its regulation of different signaling pathways, including the TGF-β, EGFR, and CD44 pathways. In this review, we seek to elucidate the signaling pathways and functions regulated by asporin in different types of cancer and to highlight some important issues that require investigation in future research.