COL1A2 is a Novel Biomarker to Improve Clinical Prediction in Human Gastric Cancer: Integrating Bioinformatics and Meta-Analysis

ceRNA network-regulated COL1A2 high expression correlates with poor prognosis and immune infiltration in colon adenocarcinoma

Collagen type I α 2 (COL1A2) is a major component of collagen type I. Recently, abnormal COL1A2 expression has been reported in human cancers. However, the specific role and mechanism of COL1A2 in colon adenocarcinoma (COAD) remain unclear. We performed the pan-cancer analysis of COL1A2 expression in 33 types of human cancers from TIMER database and integrated data combined TCGA with GTEx. The prognostic values of COL1A2 for 17 cancer types of interest were estimated from GEPIA database. The results showed that COL1A2 was significantly upregulated in COAD tissues and that higher COL1A2 expression predicted unfavorable prognosis for patients with COAD. Next, COL1A2-related functional pathways in COAD were analyzed with TCGA data using R package. Additionally, we constructed a ceRNA network that LINC00638/hsa-miR-552-3p axis served as a potential regulatory pathway of COL1A2 in COAD. Furthermore, our findings showed that COL1A2 positively associated with immune infiltration and that tumor immune escape might be involved in COL1A2-mediated carcinogenesis in COAD. For the first time, we constructed a ceRNA prediction network of COL1A2 and explored the association of COL1A2 with tumor immune microenvironment remodeling. The findings may advance our understanding of the pathogenesis mechanism in COAD and paves the way for further cancer therapeutics.

Novel CAF-identifiers via transcriptomic and protein level analysis in HNSC patients

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct "CAF-markers" which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

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.

Integrated bioinformatics analysis for exploring hub genes and related mechanisms affecting the progression of gastric cancer

Objective Gastric cancer (GC) is a high-risk tumor disease worldwide. The goal of the current study was to explore new diagnostic and prognostic indicators for gastric cancer. Methods Database GSE19826 and GSE103236 were gained from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs), which were then grouped together as co-DEGs. GO and KEGG pathway analysis were used to investigate the function of these genes. The protein-protein interaction (PPI) network of DEGs was constructed by STRING. Results GSE19826 selected 493 DEGs in GC and gastric normal tissues, including 139 up-regulated genes and 354 down-regulated genes. A total of 478 DEGs were selected by GSE103236, including 276 up-regulated genes and 202 downregulated genes. 32 co-DEGs were overlapped from two databasesand involved in digestion, regulation of response to wounding, wound healing, potassium ion imports across plasma membrane, regulation of wound healing, anatomical structure homeostasis, and tissue homeostasis. KEGG analysis revealed that co-DEGs were mainly involved in ECM-receptor interaction, tight junction, protein digestion and absorption, gastric acid secretion and cell adhesion molecules. Twelve hub genes were screened by Cytoscape, including cholecystokinin B receptor (CCKBR), Collagen type I alpha 1 (COL1A1), COL1A2, COL2A1, COL6A3, COL11A1, matrix metallopeptidase 1 (MMP1), MMP3, MMP7, MMP10, tissue inhibitor of matrix metalloprotease 1 (TIMP1) and secreted phosphoprotein 1 (SPP1). Conclusions Twelve key genes affecting the progression of gastric cancer were obtained by bioinformatics, which may be potential biomarkers for the diagnosis and prognosis of GC.

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis

OBJECTIVE

To identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).

METHODS

Four microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichment analysis (GSEA). A gene expression network was constructed using WGCNA, and a protein-protein interaction (PPI) network was used to identify the key genes. Gene modules were annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

RESULTS

We divided the cancer cases into three subgroups based on consensus clustering (subgroups I, II, III). The green module identified by WGCNA was correlated with clinical characteristics. Ten key genes were identified according to their degree of connectivity in the protein-protein interaction network: FYN, SEMA3A, AP2M1, L1CAM, NRP1, TLN1, VWF, ITGB3, ILK, and ACTN1.

CONCLUSION

We identified 10 hub genes as candidate biomarkers for CRC. These key genes may provide a theoretical basis for targeted therapy against CRC.

Identification of potential hub genes of gastric cancer

BACKGROUND

Gastric cancer (GC) is a malignant tumor originated from gastric mucosa epithelium. It is the third leading cause of cancer mortality in China. The early symptoms are not obvious. When it is discovered, it has developed to the advanced stage, and the prognosis is poor. In order to screen for potential genes for GC development, this study obtained GSE118916 and GSE109476 from the gene expression omnibus (GEO) database for bioinformatics analysis.

METHODS

First, GEO2R was used to identify differentially expressed genes (DEG) and the functional annotation of DEGs was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Search Tool for the Retrieval of Interacting Genes (STRING) tool was used to construct protein-protein interaction (PPI) network and the most important modules and hub genes were mined. Real time quantitative polymerase chain reaction assay was performed to verify the expression level of hub genes.

RESULTS

A total of 139 DEGs were identified. The functional changes of DEGs are mainly concentrated in the cytoskeleton, extracellular matrix and collagen synthesis. Eleven genes were identified as core genes. Bioinformatics analysis shows that the core genes are mainly enriched in many processes related to cell adhesion and collagen.

CONCLUSION

In summary, the DEGs and hub genes found in this study may be potential diagnostic and therapeutic targets.

Potential biomarkers and immune characteristics of small bowel adenocarcinoma

Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

CircCOL1A2 Sponges MiR-1286 to Promote Cell Invasion and Migration of Gastric Cancer by Elevating Expression of USP10 to Downregulate RFC2 Ubiquitination Level

Gastric cancers (GC) are generally malignant tumors, occurring with high incidence and threatening public health around the world. Circular RNAs (circRNAs) play crucial roles in modulating various cancers, including GC. However, the functions of circRNAs and their regulatory mechanism in colorectal cancer (CRC) remain largely unknown. This study focuses on both the role of circCOL1A2 in CRC progression as well as its downstream molecular mechanism. Quantitative polymerase chain reaction (qPCR) and western blot were adopted for gene expression analysis. Functional experiments were performed to study the biological functions. Fluorescence in situ hybridization (FISH) and subcellular fraction assays were employed to detect the subcellular distribution. Luciferase reporter, RNA-binding protein immunoprecipitation (RIP), co-immunoprecipitation (Co-IP), RNA pull-down, and immunofluorescence (IF) and immunoprecipitation (IP) assays were used to explore the underlying mechanisms. Our results found circCOL1A2 to be not only upregulated in GC cells, but that it also propels the migration and invasion of GC cells. CircCOL1A2 functions as a competing endogenous RNA (ceRNA) by sequestering microRNA-1286 (miR-1286) to modulate ubiquitin-specific peptidase 10 (USP10), which in turn spurs the migration and invasion of GC cells by regulating RFC2. In sum, CircCOL1A2 sponges miR-1286 to promote cell invasion and migration of GC by elevating the expression of USP10 to downregulate the level of RFC2 ubiquitination. Our study offers a potential novel target for the early diagnosis and treatment of GC.

Transcriptome analysis reveals SALL4 as a prognostic key gene in gastric adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) dominates 80-90% of gastric cancer (GC). Over the years, it has been realized that the identification of the genes responsible for gastric carcinogenesis is essential to understand the biomarker discovery.

METHODS

This study aims to identify candidate genes for biomarker discovery in STAD. RNA-Seq was performed on three paired tumor-normal and one unpaired tumor samples from four GC patients and investigated for differentially expressed genes (DEGs) using DESeq2. Gene set enrichment analysis were performed. The DEGs were compared with two STAD microarray datasets available on Gene Expression Omnibus (GEO) database. Survival study (OS) were performed using KM-Plotter on the common genes between all the datasets.

RESULTS

Totally, 148 DEGs were identified, wherein 55 genes were upregulated and 93 genes were downregulated with |log2foldchange| > 1 and Benjamini-Hochberg (BH) Adjusted P value < 0.01. Cell adhesion molecule (CAM) Pathway was found to be the most significant among the upregulated genes. Gastric acid secretion and mineral absorption pathways were the most significant pathways among the downregulated genes. Comparison with two GEO datasets followed by OS analysis revealed two upregulating genes, APOC1 and SALL4 with prognostic significance.

CONCLUSION

Upregulation of APOC1 is associated with marginal overall survival (OS) and SALL4 over-expression was associated with the poor OS using KM-Plotter during 5 years data period. Our study suggests that SALL4 could be a promising biomarker candidate in STAD.

Immunohistochemical expression of osteopontin and collagens in choroid plexus of human brains

Evidence showing the functional significance of the choroid plexus is accumulating. Although it is clinically well‐known that calcification is frequently seen in the choroid plexus of aged human brains, it is unclear why calcification occurs in the aged choroid plexus and what exert effects on the calcification has. In this study, immunohistochemical localizations of collagens and other molecules related to fibrosis or calcification were investigated on the choroid plexus of autopsied human brains. Densely fibrous or calcified materials were located in the stroma just below the epithelial cells of the choroid plexus of all human brains examined. Immunoreactivity for collagen type I was identified in the stroma just below the epithelial cells, consistent with the densely fibrous or calcified area, whereas that for collagen type III was observed in almost all stroma other than the densely fibrous or calcified areas. Linear or membranous immunoreactivity for collagen type IV was intermittently localized on the epithelium‐facing side of the materials, suggesting an injured basement membrane. In addition, clear immunoreactivity for osteopontin was localized on the epithelium‐facing side of the fibrous or calcified materials as well as in the cytoplasm of epithelial cells. These findings indicate that collagen type I exists in contact with osteopontin in and around the densely fibrous or calcified materials in the choroid plexus. They suggest that the densely fibrous or calcified materials are deposited in the subepithelial stroma just below an injured basement membrane of epithelial cells via the collagen type I and osteopontin.

Bioinformatics Analysis of Hub Genes and Potential Therapeutic Agents Associated with Gastric Cancer

Purpose

The current treatment methods available for advanced gastric cancer are not very promising. Hence, it is important to explore novel biomarkers and potential therapeutic agents to treat gastric cancer (GC). This study aimed to identify hub genes associated with GC prognosis and explore potential drugs for its treatment.

Materials and Methods

Three gene expression data of GC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) and processed to identify the differentially expressed genes (DEGs). We conducted a comprehensive analysis of DEGs, including functional enrichment analysis, construction of protein-protein interaction (PPI) network, identification of hub genes, survival analysis and expression verification of hub genes. Finally, we constructed the network of miRNA-mRNA, and predicted the drugs that might be effective for GC treatment.

Results

A total of 340 DEGs, including 94 up-regulated and 246 down-regulated genes, were identified. Among the up-regulated DEGs, the enrichment terms were primarily related to tumorigenesis and tumor progression, extracellular matrix organization, and collagen catabolic process. Additionally, 10 hub genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1, COL5A1, SPARC, and COL4A1) were identified, out of which 7 genes were significantly associated with poor overall survival (OS) in GC. The expression levels of these 7 hub genes were verified using real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression Profiling Interactive Analysis) server. A regulatory network of miRNA-mRNA was also constructed, and the top 4 interactive miRNAs (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p) that targeted the most hub genes were identified. Finally, fourteen small molecules were predicted to be effective in treating GC.

Conclusion

The identification of the hub genes, miRNA-mRNA network, and potential candidate drugs associated with GC provides new insights into the molecular mechanisms and treatment of GC.

Identification of three tumor antigens and immune subtypes for mRNA vaccine development in diffuse glioma

Rationale: Diffuse glioma patients have high mortality and recurrence despite multimodal therapies. This study aims to identify the potential tumor antigens for mRNA vaccines and subtypes suitable for the immunotherapy of patients with diffuse glioma. Methods: Gene expression profiles and corresponding clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) databases. Genetic alterations were extracted from cBioPortal. Differential gene analysis, survival analysis, correlation analysis, consensus clustering analysis, and immune cell infiltration analysis were conducted based on the various databases. Finally, the hub genes, the modules related to tumor antigens, and the immune subtypes were identified using WGCNA method. Results: Three over-expressed, amplified, and mutated tumor antigens, including KDR, COL1A2, and SAMD9, were associated with clinical outcomes. The expression of the three genes had a positive correlation with the abundance of antigen-presenting cells (APCs) and APC marker expression. Subsequently, three immune subtypes (Ims1, Ims2, and Ims3) were distinguished in the TCGA cohort, which exhibited distinct molecular, cellular, and clinical characteristics consistent with the CGGA cohort. Diffuse gliomas with subtype Ims1 were more malignant with immunosuppressive phenotypes and more associated with poor prognosis than the other two subtypes. The three antigens and the immune checkpoints were differentially expressed among the three immune subtypes. Finally, functional enrichment analysis of the genes related to tumor antigens and immune subtypes suggested that they are enriched in many immune-associated processes. Conclusions: KDR, COL1A2, and SAMD9 are potential antigens for developing mRNA vaccines against diffuse glioma. The results suggest that immunotherapy targeting these three antigens is more suitable for patients with subtype Ims1. This study provides insights into immunotherapy for diffuse glioma.

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

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

Glucoside xylosyltransferase 2 as a diagnostic and prognostic marker in gastric cancer via comprehensive analysis

To investigate the potential role of GXYLT2 (glucoside xylosyltransferase 2) in gastric cancer (GC), the TCGA (The Cancer Genome Atlas) database and Gene Expression Omnibus (GEO) dataset were used to evaluate GXYLT2 mRNA expression, and the standardized mean difference and diagnostic value were comprehensively assessed. Survival analysis and univariate/multivariate cox regression analysis were performed to evaluate the prognostic value of GXYLT2 in GC patients. The correlation between GXYLT2 and tumor immune cells was identified by using the CIBERSORT algorithm. The results showed that GXYLT2 expression level was significantly increased in GC tissues. GXYLT2 expression was significantly correlated with the grade, stage, and invasion depth of gastric cancer. Overall survival was reduced in the high GXYLT2 expression group. Univariate and multivariate Cox regression analyses showed that GXYLT2 was a reliable prognostic factor. GSEA showed that GXYLT2 might participate in the development of GC through tumor-related pathways. The expression of GXYLT2 was positively correlated with 5 tumor-infiltrating immune cells (resting dendritic cells, m2 macrophages, monocytes, active NK cells and resting mast cells), and was negatively correlated with 6 tumor-infiltrating immune cells (plasma cells, activated memory CD4 T cells, resting NK cells, activated dendritic cells, and activated neutrophils and mast cells). Through cell experiment verification, GXYLT2 expression level in gastric cancer cells was found to be high, which verified the results from the bioinformatics analysis. Furthermore, immunohistochemical staining results also showed that GC tissues had positive GXYLT2 expression. In summary, GXYLT2 might be a potential diagnostic and prognostic biomarker for gastric cancer.

Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia

Background

Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis.

Methods

Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes.

Results

360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10, included in the models.

Conclusion

CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

Current Insights on the Diverse Structures and Functions in Bacterial Collagen-like Proteins

The dearth of knowledge on the diverse structures and functions in bacterial collagen-like proteins is in stark contrast to the deep grasp of structures and functions in mammalian collagen, the ubiquitous triple-helical scleroprotein that plays a central role in tissue architecture, extracellular matrix organization, and signal transduction. To fill and highlight existing gaps due to the general paucity of data on bacterial CLPs, we comprehensively reviewed the latest insight into their functional and structural diversity from multiple perspectives of biology, computational simulations, and materials engineering. The origins and discovery of bacterial CLPs were explored. Their genetic distribution and molecular architecture were analyzed, and their structural and functional diversity in various bacterial genera was examined. The principal roles of computational techniques in understanding bacterial CLPs' structural stability, mechanical properties, and biological functions were also considered. This review serves to drive further interest and development of bacterial CLPs, not only for addressing fundamental biological problems in collagen but also for engineering novel biomaterials. Hence, both biology and materials communities will greatly benefit from intensified research into the diverse structures and functions in bacterial collagen-like proteins.

COL5A2 as a potential clinical biomarker for gastric cancer and renal metastasis

BACKGROUND

Gastric cancer, characterized by insidious onset and multiple metastasis, is almost incurable and has poor prognosis, and also one of the leading causes of treatment failure and death in patients with gastric cancer (GC). However, the prognosis of collagen type V alpha2 chain (COL5A2) in GC and renal metastasis is unknown.

METHODS

Recruited 148 patients who underwent GC. The diagnosis of GC was confirmed by ultrasound imaging and pathological examination. Immunohistochemistry and RT-qPCR were performed to exam the expression level of COL5A2. The statistical methods included Pearson chi-square test, Spearman-rho correlation test, univariate and multivariate cox regression analysis. Finally, this research constructed receiver operating characteristic (ROC) curves and applied the area under the curve (AUC).

RESULTS

Based on Pearson's chi-square test, Spearman-rho test, and univariate/multivariate cox regression, pathologic grade (P < .001), renal metastasis (P < .001) and staging (P < .001) were significantly related to COL5A2. And COL5A2 expression (hazard ratio [HR]: 18.834, P < .001) is an independent risk factor of GC. The AUC was used as the degree of confidence in judging each factor: COL5A2 (AUC = 0.878, P < .001), COL1A1 (AUC = 0.636, P = .006), COL1A2 (AUC = 0.545, P = .368), and COL3A1 (AUC = 0.617, P = .019). Through the ROC result, COL5A2 had more advantage as a biomarker for GC than other collagens.

CONCLUSIONS

COL5A2 gene expression level might be a risk factor for GC. COL5A2 has a strong correlation with the prognosis of the disease.

Transcriptome Analysis of MDA-MB-231 Cells Treated with Fumosorinone Isolated from Insect Pathogenic Fungi

BACKGROUND

In our previous study, we have isolated a new compound, named Fumosorinone (FU) from insect pathogenic fungi, and was found to inhibit proliferation, migration, and invasion of breast cancer MDA-MB-231 cells.

OBJECTIVE

The aim of this study was to identify the underlying molecular mechanisms for FU effects on MDAMB- 231 cells.

METHODS

After MDA-MB-231 cells were treated with FU for 48h, RNA sequencing was used to identify the effect of FU on the transcriptome of MDA-MB-231 cells. The validation of the relative expression of the selective genes was done using quantitative real-time PCR (qRT-PCR).

RESULTS

The transcriptome results showed that 2733 genes were differentially expressed between the untreated and the FU-treated cells, including 1614 up-regulated and 1119 down-regulated genes. The multiple genes are associated with cancer cell growth, migration, and invasion. Functional analysis identified multitude of pathways related to cancer, such as cell cycle, ECM-receptor interaction, p53 signaling pathway. We selected 4 upregulated and 9 downregulated genes, which are associated with breast cancer to verify their expression using qRT-PCR. The validation showed that HSD3B1, ALOX5, AQP5, COL1A2, CCNB1, CCND1, VCAM-1, PTPN1 and PTPN11 were significantly downregulated while DUSP1, DUSP5, GADD45A, EGR1 were upregulated in FU-treated MDA-MB-231cells.

CONCLUSION

These aberrantly expressed genes and pathways may play pivotal roles in the anti-cancer activity of FU, and maybe potential targets of FU treatments for TNBC. Further investigations are required to evaluate the FU mechanisms of anti-cancer action in vivo.

LncRNA LIFR-AS1 promotes proliferation and invasion of gastric cancer cell via miR-29a-3p/COL1A2 axis

BACKGROUND

LncRNA was known to be closely associated with the progression of human tumors. The role of lncRNA LIFR-AS1 in the pathogenesis and progression of gastric tumor is still unclear. The aim of this study was to investigate the function of LIFR-AS1 and the underlying mechanism in the pathogenesis and progression of gastric cancer.

METHODS

QRT-PCR was used to evaluate the expression of LIFR-AS1, miR-29a-3p and COL1A2 in gastric tumor tissues and cells. Western blotting was used to evaluate the protein expression of COL1A2 in gastric tumor cells. CCK-8 assay, transwell assay and flow cytometry were used to evaluate the roles of LIFR-AS1, miR-29a-3p and COL1A2 in cell proliferation, invasion, migration and apoptosis. The relationship among LIFR-AS1, miR-29a-3p and COL1A2 was assessed by bioinformatics analyses and luciferase reporter assay.

RESULTS

The expression levels of LIFR-AS1 were significantly increased in gastric tumor tissues and cells, while the expression levels of miR-29a-3p were decreased. The expression of miR-29a-3p was negatively correlated with the expression of LIFR-AS1 in gastric cancer tumor tissues. Knocking down of LIFR-AS1 inhibited proliferation, invasion and migration of gastric tumor cells, and induced apoptosis of gastric tumor cells. Bioinformatics analyses and integrated experiments revealed that LIFR-AS1 elevated the expression of COL1A2 through sponging miR-29a-3p, which further resulted in the progression of gastric tumor.

CONCLUSION

LIFR-AS1 plays an important role as a competing endogenous RNA in gastric tumor pathogenesis and may be a potential target for the diagnosis and treatment of gastric tumor.

Biomarkers of tumor invasiveness in proteomics (Review)

Over the past two decades, quantitative proteomics has emerged as an important tool for deciphering the complex molecular events involved in cancers. The number of references involving studies on the cancer metastatic process has doubled since 2010, while the last 5 years have seen the development of novel technologies combining deep proteome coverage capabilities with quantitative consistency and accuracy. To highlight key findings within this huge amount of information, the present review identified a list of tumor invasive biomarkers based on both the literature and data collected on a biocollection of experimental cell lines, tumor models of increasing invasiveness and tumor samples from patients with colorectal or breast cancer. Crossing these different data sources led to 76 proteins of interest out of 1,245 mentioned in the literature. Information on these proteins can potentially be translated into clinical prospects, since they represent potential targets for the development and evaluation of innovative therapies, alone or in combination. Herein, a systematical review of the biology of each of these proteins, including their specific subcellular/extracellular or multiple localizations is presented. Finally, as an important advantage of quantitative proteomics is the ability to provide data on all these molecules simultaneously in cell pellets, body fluids or paraffin‑embedded sections of tumors/invaded tissues, the significance of some of their interconnections is discussed.

Identifying multiple collagen gene family members as potential gastric cancer biomarkers using integrated bioinformatics analysis

Background

Gastric cancer is one of the most common malignant cancers worldwide. Despite substantial developments in therapeutic strategies, the five-year survival rate remains low. Therefore, novel biomarkers and therapeutic targets involved in the progression of gastric tumors need to be identified.

Methods

We obtained the mRNA microarray datasets GSE65801, GSE54129 and GSE79973 from the Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to analyze DEG pathways and functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape to obtain the protein-protein interaction (PPI) network. Next, we validated the hub gene expression levels using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA), and conducted stage expression and survival analysis.

Results

From the three microarray datasets, we identified nine major hub genes: COL1A1, COL1A2, COL3A1, COL5A2, COL4A1, FN1, COL5A1, COL4A2, and COL6A3.

Conclusion

Our study identified COL1A1 and COL1A2 as potential gastric cancer prognostic biomarkers.

Clinical significance of COL1A1 and COL1A2 expression levels in hypopharyngeal squamous cell carcinoma

Alterations in collagen type I α1 (COL1A1) and collagen type I α 2 (COL1A2) expression levels have been reported to predict prognosis in various types of cancer. However, the effect of these biomarkers on hypopharyngeal squamous cell carcinoma (HPSCC) is yet to be fully elucidated. The present study aimed to explore the prognostic significance of COL1A1 and COL1A2 expression levels in HPSCC. The expression levels of COL1A1 and COL1A2 in 67 patients with HPSCC were examined using an immunohistochemical assay in a tissue microarray. The associations between COL1A1/COL1A2 expression levels and patient clinicopathological features were analyzed using ANOVA, Pearson's χ² or Fisher's exact test. The Cox proportional hazard models and Kaplan-Meier survival analysis with log-rank tests were used to analyze the significance of COL1A1/COL1A2 as prognostic markers for patients with HPSCC. As a result, immunohistochemical staining revealed that COL1A1 was positively expressed in all cases, among which 40.3% were strong positive, while COL1A2 was positively expressed in 76.1% of the HPSCC cases with 6.0% of the samples exhibiting strong staining. Further analysis revealed no significant association between the expression levels of COL1A1/COL1A2 and other clinicopathological features. Cox regression analysis revealed that a high COL1A2 expression level predicted a high locoregional recurrence and a less favorable disease-free survival rate (P=0.042 and 0.020, respectively). Overall, the present study indicated that COL1A2 expression levels may have value as a prognostic indicator in HPSCC.

PHTF2 regulates lipids metabolism in gastric cancer

Identification of hub genes and key pathways of gastric cancer was recognized to be essential to elucidate the tumorigenesis of GC. This study was aimed to identify the differentially expressed genes (DEGs) in GC via bioinformatics methods and their related pathways involved in the pathological process of GC. Gene expression profile datasets acquired by microarray chips or RNA-seq were downloaded from GEO dataset and TCGA, and 298 differentially expressed genes was identified. The Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) pathways of DEGs were then analyzed by the DAVID database to elucidate the potential molecular functions of DEGs. The protein-protein interaction (PPI) network of DEGs was further analyzed with the STRING database and PHTF2 was identified as a hub gene in the PPI network. Subsequently, PHTF2 was found to be highly expressed in different subtypes of gastric cancer tissues obtained from TCGA database or clinical patients, resulting with a poor prognosis. By GSEA, PHTF2 was found to significantly enrich the fatty acid metabolism pathway in gastric cancer. Moreover, PHTF2-regulated lipids metabolism significantly affected the tumorigenesis of GC cells. In summary, this work identified a new mechanism by which PHTF2 precipitated in the pathological process of GC by regulating cellular lipid metabolism.

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

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

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

Identification of Key Genes and Signaling Pathways Associated with the Progression of Gastric Cancer

Genomic features have been gradually regarded as part of the fundamentals to the clinical diagnosis and treatment for gastric cancer. However, the molecular alterations taking place during the progression of gastric cancer remain unclear. Therefore, identification of potential key genes and pathways in the gastric cancer progression is crucial to clinical practices. The gene expression profile, GSE103236, was retrieved for the identification of the differentially expressed genes (DEGs), followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments, gene set enrichment analysis (GSEA) and the protein-protein interaction (PPI) networks. Multiple bioinformatics platforms were employed for expression and prognostic analysis. Fresh frozen gastric cancer tissues were used for external validation. A total of 161 DEGs were identified from GSE103236. The PPI network-derived hub genes included collagen type I alpha 1 chain (COL1A1), tissue inhibitor of the metalloproteinases (TIMP1), Secreted Phosphoprotein 1 (SPP1), somatostatin (SST), neuropeptide Y (NPY), biglycan (BGN), matrix metallopeptidase 3 (MMP3), apolipoprotein E (APOE), ATPase H+/K+ transporting alpha subunit (ATP4A), lysyl oxidase (LOX). SPP1 (log rank p = 0.0048, HR = 1.39 [1.1-1.75]) and MMP3 (log rank p < 0.0001, HR = 1.77 [1.44-2.19]) were significantly associated with poor overall survival. Stage-specifically, both COL1A1 and BGN were correlated with significant in stage III and IV gastric cancer cases. LOX showed significant correlation with prognosis in stage I and stage II gastric cancer cases. Furthermore, cg00583003 of SPP1 and cg16466334 of MMP3 exhibited highly methylation level and significant prognostic values (SPP1: HR = 1.625, p = 0.013; MMP3: HR = 0.647, p = 0.011). Hub genes signature displayed a favorable prognostic value (p value = 5.227e-05). APOE demonstrated the highest correlation with CD8⁺ T cells, neutrophils, and dendritic cells whereas BGN had the highest correlation with macrophages. This study systematically explored the key genes and pathways involved in PGC and AGC, providing insights into therapeutic individualized management.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

The clinical significance of collagen family gene expression in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality. Due to the poor 5-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent. Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition. However, the clinical significance of collagen genes in ESCC has been rarely studied. In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers.

METHODS

Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas and the gene expression omnibus databases. Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and co-expression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC.

RESULTS

A total of 22 genes of collagen family were identified as differentially expressed genes in both the two datasets. Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated. Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival. In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis staging based model. Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc.

CONCLUSION

Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC. Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients. These findings may facilitate early detection of ESCC and help improves prognosis of the patients.

Integrated bioinformatics analysis of As, Au, Cd, Pb and Cu heavy metal responsive marker genes through Arabidopsis thaliana GEO datasets

BACKGROUND

Current environmental pollution factors, particularly the distribution and diffusion of heavy metals in soil and water, are a high risk to local environments and humans. Despite striking advances in methods to detect contaminants by a variety of chemical and physical solutions, these methods have inherent limitations such as small dimensions and very low coverage. Therefore, identifying novel contaminant biomarkers are urgently needed.

METHODS

To better track heavy metal contaminations in soil and water, integrated bioinformatics analysis to identify biomarkers of relevant heavy metal, such as As, Cd, Pb and Cu, is a suitable method for long-term and large-scale surveys of such heavy metal pollutants. Subsequently, the accuracy and stability of the results screened were experimentally validated by quantitative PCR experiment.

RESULTS

We obtained 168 differentially expressed genes (DEGs) which contained 59 up-regulated genes and 109 down-regulated genes through comparative bioinformatics analyses. Subsequently, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of these DEGs were performed, respectively. GO analyses found that these DEGs were mainly related to responses to chemicals, responses to stimulus, responses to stress, responses to abiotic stimulus, and so on. KEGG pathway analyses of DEGs were mainly involved in the protein degradation process and other biologic process, such as the phenylpropanoid biosynthesis pathways and nitrogen metabolism. Moreover, we also speculated that nine candidate core biomarker genes (namely, NILR1, PGPS1, WRKY33, BCS1, AR781, CYP81D8, NR1, EAP1 and MYB15) might be tightly correlated with the response or transport of heavy metals. Finally, experimental results displayed that these genes had the same expression trend response to different stresses as mentioned above (Cd, Pb and Cu) and no mentioned above (Zn and Cr).

CONCLUSION

In general, the identified biomarker genes could help us understand the potential molecular mechanisms or signaling pathways responsive to heavy metal stress in plants, and could be applied as marker genes to track heavy metal pollution in soil and water through detecting their expression in plants growing in those environments.

P4HA3 is Epigenetically Activated by Slug in Gastric Cancer and its Deregulation is Associated With Enhanced Metastasis and Poor Survival

Prolyl 4-hydroxylase alpha subunit is the enzymic active site of prolyl 4-hydroxylase, which is a critical enzyme to maintain the stability of newly synthesized collagens. The expression profile and functional role of P4HA3 in gastric cancer have not been explored. In the Cancer Genome Atlas-Stomach Cancer, P4HA3 RNA is significantly upregulated in gastric cancer than in normal stomach tissues. In the Human Protein Atlas, Prolyl 4-hydroxylase alpha subunit is not detectable by immunohistochemistry staining in normal stomach tissues, but it has weak staining in 7 of 12 gastric cancer tissues. Further study showed that SNAI2 (encoding Slug) is highly coexpressed with P4HA3 (Pearson r = 0.70) in Cancer Genome Atlas-Stomach Cancer. In vitro cell assay showed that Slug could efficiently bind to the P4HA3 promoter and increase its transcription. P4HA3 exon array data in Cancer Genome Atlas-Stomach Cancer revealed that 2 exons are significantly upregulated in M1 (N = 27) cases than in M0 (N = 367) cases. In MKN-45 and AGS cells, P4HA3 upregulation could enhance cell motility and invasiveness. In Cancer Genome Atlas-Stomach Cancer, high P4HA3 exon expression is associated with significantly worse 5-year and 10-year overall survival (P = .007 and .009, respectively). Data mining in Kaplan-Meier plotter also showed that high P4HA3 expression is related to unfavorable overall survival (hazard ratio: 1.54, 95% confidence interval: 1.23-1.93, P < .001) and first progression-free survival (hazard ratio: 1.64, 95% confidence interval: 1.29-2.1, P < .001). Based on findings above, we infer that P4HA3 is epigenetically activated by Slug, and its deregulation is associated with enhanced metastasis and poor survival of gastric cancer.

Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

Objective

Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.

Methods

The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.

Results

A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.

Conclusion

In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.