BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

The dawn of a new Era: mRNA vaccines in colorectal cancer immunotherapy

Colorectal cancer (CRC) is a typical cancer that accounts for 10% of all new cancer cases annually and nearly 10% of all cancer deaths. Despite significant progress in current classical interventions for CRC, these traditional strategies could be invasive and with numerous adverse effects. The poor prognosis of CRC patients highlights the evident and pressing need for more efficient and targeted treatment. Novel strategies regarding mRNA vaccines for anti-tumor therapy have also been well-developed since the successful application for the prevention of COVID-19. mRNA vaccine technology won the 2023 Nobel Prize in Physiology or Medicine, signaling a new direction in human anti-cancer treatment: mRNA medicine. As a promising new immunotherapy in CRC and other multiple cancer treatments, the mRNA vaccine has higher specificity, better efficacy, and fewer side effects than traditional strategies. The present review outlines the basics of mRNA vaccines and their advantages over other vaccines and informs an available strategy for developing efficient mRNA vaccines for CRC precise treatment. In the future, more exploration of mRNA vaccines for CRC shall be attached, fostering innovation to address existing limitations.

Identification of Key Genes Associated with Tumor Microenvironment Infiltration and Survival in Gastric Adenocarcinoma via Bioinformatics Analysis

OBJECTIVE

Gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development.

METHODS

We employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database.

RESULTS

Cytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells.

CONCLUSION

Our findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients.

COMP Improves Ang-II-Induced Atrial Fibrillation via TGF-β Signaling Pathway

Cartilage oligomeric matrix protein (COMP) regulates transforming growth factor-β (TGF-β) signaling pathway, which has been proved to be associated with skin fibrosis and pulmonary fibrosis. Atrial fibrosis is a major factor of atrial fibrillation (AF). Nevertheless, the interaction between COMP and TGF-β as well as their role in AF remains undefined. The purpose of this study is to clarify the role of COMP in AF and explore its potential mechanism. The hub gene of AF was identified from two datasets using bioinformatics. Furthermore, it was verified by the downregulation of COMP in angiotensin-II (Ang-II)-induced AF in mice. Moreover, the effect on AF was examined using CCK8 assay, ELISA, and western blot. The involvement of TGF-β pathway was further discussed. The expression of COMP was the most significant among all these hub genes. Our experimental results revealed that the protein levels of TGF-β1, phosphorylated Smad2 (P-Smad2), and phosphorylated Smad3 (P-Smad3) were decreased after silencing COMP, which indicated that COMP knockdown could inhibit the activation of TGF-β pathway in AF cells. However, the phenomenon was reversed when the activator SRI was added. COMP acts as a major factor and can improve Ang-II-induced AF via TGF-β signaling pathway. Thus, our research enriches the understanding of the interaction between COMP and TGF-β in AF, and provides reference for the pathogenesis and diagnosis of AF.

Identifying Crucial Biomarkers in Osteoporosis and Ulcerative Colitis Through Bioinformatics Analysis of Co-expressed Genes

Osteoporosis (OP) and ulcerative colitis (UC), prevalent immune diseases, exert a substantial socioeconomic impact globally. This study identifies biomarkers for these diseases, paving the way for in-depth research. Initially, the Gene Expression Omnibus (GEO) database was employed to analyze datasets GSE35958 and GSE87466. This analysis aimed to pinpoint co-expression differential genes (DEGs) between OP and UC. Subsequently, the Metascape database facilitated the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs' co-expression. For network construction and visualization, the STRING11.5 database along with Cytoscape 3.7.2 (Cytoscape Team, USA) were utilized to create a protein-protein interaction (PPI) network. Moreover, Cytoscape's cytoHubba plugin was instrumental in identifying the central genes, known as hub genes. In the datasets GSE35958 and GSE87466, 156 co-expressed DEGs were discovered. The PPI network, constructed using STRING11.5 and Cytoscape 3.7.2, comprises 96 nodes and 222 connections. Notably, seven hub genes were identified, namely COL6A1, COL6A2, BGN, NID1, PLAU, TGFB1, and PLAUR. These DEGs were predominantly enriched in pathways such as extracellular matrix organization and collagen-containing extracellular matrix, as per GO analysis. For diagnostic model construction and hub gene validation, datasets GSE56815 and GSE107499 from the GEO database were employed. The top five hub genes were validated. In conclusion, the hub genes identified in this study played a significant role in the early diagnosis, prevention, and treatment of OP and UC. Furthermore, they provide fresh insights into the underlying mechanisms of these diseases' development and progression.

Relationship of biglycan and decorin expression with clinicopathological features and prognosis in patients with oral squamous cell carcinoma

OBJECTIVE

This study focused on investigating relation between biglycan (BGN) and decorin (DCN) expression and prognostic outcome for oral squamous cell carcinoma (OSCC) cases.

MATERIAL AND METHODS

BGN and DCN mRNA and protein expression was detected by qRT-PCR and Western-blotting (WB) assays from 31 OSCC samples as well as healthy samples. This work harvested 101 paraffin-embedded OSCC together with 30 healthy samples, and conducted immunohistochemical (IHC) staining for assessing pathological changes. Association of DCN with BGN within OSCC was explored by Spearman's analysis. Survival rate was explored by Kaplan-Meier (KM) approach. Multivariate analysis was conducted by Cox regression.

RESULTS

WB and qRT-PCR results showed BGN up-regulation (p < 0.001, p < 0.0001) whereas DCN down-regulation (p < 0.0001, p < 0.0001) with fresh OSCC tissues; the expression of BGN and DCN associated with the OSCC histopathological grade. IHC results suggested elevated BGN level (p < 0.0001) whereas DCN down-regulation (p < 0.0001) with paraffin embedded OSCC tissues. The expression of BGN and DCN associated with histopathologic grades and tumor stage of OSCC. The result of Spearman's analysis demonstrated significant association between the expression of BGN and DCN in OSCC. Survival analysis revealed that patients with higher BGN/lower DCN level showed poor overall survival (OS) as well as tumor-specific survival (TSS). Multivariate analysis proved that BGN and DCN independently predicted the prognosis of OS and TSS.

CONCLUSION

BGN and DCN expression levels can be adopted for predicting OSCC prognostic outcome.

Identification of BGN and THBS2 as metastasis-specific biomarkers and poor survival key regulators in human colon cancer by integrated analysis

BACKGROUND

Colon cancer is the second leading cause of death worldwide. Exploring key regulators in colon cancer metastatic progression could lead to better outcomes for patients.

METHODS

Initially, the transcriptional profiles of 681 colonrectal cancer (CRC) cases were used to discover signature genes that were significantly correlated with colon cancer metastasis. These signature genes were then validated using another independent 210 CRC cases' transcriptomics and proteomics profiles, and Kaplan-Meier regression analyses were used to screen the key regulators with patients' survival. Immunohistochemical staining was used to confirm the biomarkers, and transit knockdown was used to explore their implications on colon cancer cells migration and invasion abilities. The impact on the key signalling molecules in epithelial-mesenchymal transition (EMT) process that drive tumour metastasis was tested using Western blot. The response to clinical standard therapeutic drugs was compared to clinical prognosis of key regulators using an ROC plotter.

RESULTS

Five genes (BGN, THBS2, SPARC, CDH11 and SPP1) were initially identified as potential biomarkers and therapeutic targets of colon cancer metastasis. The most significant signatures associated with colon cancer metastasis were determined to be BGN and THBS2. Furthermore, highly expression of BGN and THBS2 in tumours was linked to a worse survival rate. BGN and THBS2 knockdown significantly reduced colon cancer cells migration and invasion, as well as down-regulating three EMT-related proteins (Snail, Vimentin and N-cadherin), and increasing the proliferation inhibitory effect of 5-fluorouracil, irinotecan and oxaliplatin treatment.

CONCLUSIONS

CRC metastatic progression, EMT phenotypic transition and poor survival time have been linked to BGN and THBS2. They could be utilized as potential diagnostic and therapeutic targets for colon cancer metastatic patients with a better prognosis.

Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA

Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.

BGN May be a Potential Prognostic Biomarker and Associated With Immune Cell Enrichment of Gastric Cancer

Background: Biglycan (BGN) plays a role in the occurrence and progression of several malignant tumors, though its role in gastric cancer (GC) remains unclear. The objective of this study was to investigate BGN expression, its role in GC prognosis, and immune infiltration. Material and Methods: Gene expression data and corresponding clinical information were downloaded from TCGA and GTEx, respectively. We compared the expression of BGN in GC and normal tissues and verified the differential expression via Real-Time PCR and immunohistochemistry. BGN-related differentially expressed genes (DEGs) were identified. Additionally, the relationships between BGN gene expression and clinicopathological variables and survival in patients with GC were also investigated through univariate and multivariate Cox regression analyses. Finally, we established a predictive model that could well predict the probability of 1-, 3-, and 5-years survival in GC. Results: We found a significantly higher expression of BGN in GC than that in normal tissues (p < 0.001), which was verified by Real-Time PCR (p < 0.01) and immunohistochemistry (p < 0.001). The 492 identified DEGs were primarily enriched in pathways related to tumor genesis and metastasis, including extracellular matrix (ECM)-receptor interaction, focal adhesion pathway, Wnt signaling, and signaling by VEGF. BGN expression was positively correlated with the enrichment of the NK cells (r = 0.620, p < 0.001) and macrophages (r = 0.550, p < 0.001), but negatively correlated with the enrichment of Th17 cells (r = 0.250, p < 0.001). BGN expression was also significantly correlated with histologic grade (GI&G2 vs. G3, p < 0.001), histologic type (Diffuse type vs. Tubular type, p < 0.001), histologic stage (stage I vs. stage II and stage I vs. stage III, p < 0.001), T stage (T1 vs. T2, T1 vs. T3, and T1 vs. T4, p < 0.001) and Helicobacter pylori (HP) infection (yes vs. no, p < 0.05) in GC. High BGN expression showed significant association with poor overall survival (OS) in GC patients (HR = 1.53 (1.09-2.14), p = 0.013). The constructed nomogram can well predict the 1-, 3-, and 5-years overall survival probability of GC patients (C-index = 0.728). Conclusion: BGN plays an important role in the occurrence and progression of GC and is a potential biomarker for the diagnosis and treatment of GC.

Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy

Background

Colon cancer is one of the most frequent malignancies and causes high mortality worldwide. Exploring the tumor-immune interactions in the tumor microenvironment and identifying new prognostic and therapeutic biomarkers will assist in decoding the novel mechanism of tumor immunotherapy. BGN is a typical extracellular matrix protein that was previously validated as a signaling molecule regulating multiple processes of tumorigenesis. However, its role in tumor immunity requires further investigation.

Methods

The differentially expressed genes in three GEO datasets were analyzed, and BGN was identified as the target gene by intersection analysis of PPIs. The relevance between clinical outcomes and BGN expression levels was evaluated using data from the GEO database, TCGA and tissue microarray of colon cancer samples. Univariable and multivariable Cox regression models were conducted for identifying the risk factors correlated with clinical prognosis of colon cancer patients. Next, the association between BGN expression levels and the infiltration of immune cells as well as the process of the immune response was analyzed. Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.

Results

BGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. Elevated BGN was associated with shorter overall survival as well as unfavorable clinicopathological features, including tumor size, serosa invasion and length of hospitalization. Mechanistically, pathway enrichment and functional analysis demonstrated that BGN was positively correlated with immune and stromal scores in the TME and primarily involved in the regulation of immune response. Further investigation revealed that BGN was strongly expressed in the immunosuppressive phenotype and tightly associated with the infiltration of multiple immune cells in colon cancer, especially M2 macrophages and induced Tregs. Finally, we demonstrated that high BGN expression presented a better immunotherapeutic response in colon cancer patients.

Conclusion

BGN is an encouraging predictor of diagnosis, prognosis and immunotherapeutic response in patients with colon cancer. Assessment of BGN expression represents a novel approach with great promise for identifying patients who may potentially benefit from immunotherapy.

TRIP13, identified as a hub gene of tumor progression, is the target of microRNA-4693-5p and a potential therapeutic target for colorectal cancer

Colorectal cancer (CRC) is one of the digestive tract malignancies whose early symptoms are not obvious. This study aimed to identify novel targets for CRC therapy, especially early-stage CRC, by reanalyzing the publicly available GEO and TCGA databases. Thyroid hormone receptor interactor 13 (TRIP13) correlated with tumor progression and prognosis of patients after several rounds of analysis, including weighted gene correlation network analysis (WGCNA), and further chosen for experimental validation in cancer cell lines and patient samples. We identified that mRNA and protein levels of TRIP13 increased in CRC cells and tumor tissues with tumor progression. miR-4693-5p was significantly downregulated in CRC tumor tissues and bound to the 3′ untranslated region (3′UTR) of TRIP13, downregulating TRIP13 expression. DCZ0415, a small molecule inhibitor targeting TRIP13, induced anti-tumor activity in vitro and in vivo. DCZ0415 markedly suppressed CRC cell proliferation, migration, and tumor growth, promoted cell apoptosis, and resulted in the arrest of the cell cycle. Our research suggests that TRIP13 might play a crucial role in CRC progression and could be a potential target for CRC therapy.

Identification of Tumor Antigens and Design of mRNA Vaccine for Colorectal Cancer Based on the Immune Subtype

mRNA vaccines have become a promising alternative to conventional cancer immunotherapy approaches. However, its application on colorectal cancer (CRC) remains poorly understood. We herein identified potential antigens for designing an effective mRNA vaccine, further to build an immune landscape for the accurate selection of patients for mRNA vaccine therapy. Raw transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrieved. Consensus clustering algorithm was applied to divide the CRC samples into four immune subtypes. Immunogenomics analysis was further integrated to characterize the immune microenvironment of each immune subtype. Six tumor antigens were found to be associated with poor prognosis and infiltration of antigen-presenting cells (APCs) in CRC patients. Furthermore, each of the immune subtypes showed differential cellular and molecular features. The IS2 and IS4 exhibited significantly improved survival and higher immune cell infiltration compared with IS1 and IS3. Immune checkpoint molecules and human leukocyte antigen also showed significant differential expression in four immune subtypes. Moreover, we performed graph structure learning-based dimensionality reduction to visualize the immune landscape of CRC. Our results revealed a complex immune landscape that may provide directions for mRNA vaccine treatment of CRC and define appropriate vaccination patients.

Hypoxia-related prognostic model in bladder urothelial reflects immune cell infiltration

Hypoxia is a common feature of tumor microenvironment (TME). This study aims to establish the genetic features related to hypoxia in Bladder urothelial carcinoma (BLCA) and investigate the potential correlation with hypoxia in the TME and immune cells. We established a BLCA outcome model using the hypoxia-related genes from The Cancer Genome Atlas using regression analysis and verified the model using the Gene Expression Omnibus GSE32894 cohort. We measured the effect of each gene in the hypoxia-related risk model using the Human Protein Atlas website. The predictive abilities were compared using the area under the receiver operating characteristic curves. Gene Set Enrichment Analysis was utilized for indicating enrichment pathways. We analyzed immune cell infiltration between risk groups using the CIBERSORT method. The indicators related to immune status between the two groups were also analyzed. The findings indicated that the high-risk group had better outcomes than the low-risk group in the training and validation sets. Each gene in the model affected the survival of BLCA patients. Our hypoxia-related risk model had better performance compared to other hypoxia-related markers (HIF-1α and GLUT-1). The high-risk group was enriched in immune-related pathways. The expression of chemokines and immune cell markers differed significantly between risk groups. Immune checkpoints were more highly expressed in the high-risk group. These findings suggest that the hypoxia-related risk model predicts patients' outcomes and immune status in BLCA risk groups. Our findings may contribute to the treatment of BLCA.

Assessment of cancer prevention effect of exercise

There are many documents about benefits of exercise on human health. However, evidences indicate to positive effect of exercise on disease prevention, understanding of many aspects of this mechanism need more investigations. Determination of critical genes which effect human health.GSE156249 including 12 gene expression profiles of healthy individual biopsy from vastus lateralis muscle before and after 12-week combined exercise training intervention were extracted from gene expression omnibus (GEO) database. The significant DEGs were included in interactome unit by Cytoscape software and STRING database. The network was analyzed to find the central nodes subnetwork clusters. The nodes of prominent cluster were assessed via gene ontology by using ClueGO. Number of 8 significant DEGs and 100 first neighbors analyzed via network analysis. The network includes 2 clusters and COL3A1, BGN, and LOX were determined as central DEGs. The critical DEGs were involved in cancer prevention process.

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

Purpose

In this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).

Methods

mRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.

Results

mRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (^∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.

Conclusions

GA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.