An Analysis Regarding the Association Between the ISLR Gene and Gastric Carcinogenesis

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

Integrated comparison of the mRNAome in cartilage, synovium, and macrophages in osteoarthritis

The precise molecular mechanisms associated with osteoarthritis (OA), the most common musculoskeletal disorder, are poorly understood. There are currently no effective treatments to prevent the initiation and progression of the disease. In recent years, the development of mRNAome has made it possible to identify new mechanisms and therapeutic targets. However, the differentially expressed genes screened by different microarrays are not completely the same. In order to avoid this shortcoming, we integrate the different genes from different tissues and data sets, and select the commonly expressed genes for further studies.

Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis

Objective

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms.

Methods

Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined.

Results

A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils.

Conclusion

COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.

ISLR interacts with MGAT5 to promote the malignant progression of human gastric cancer AGS cells

Objectives

Gastric cancer is a common malignant tumor with high morbidity and mortality. The present study aimed to investigate the role of the immunoglobulin superfamily containing leucine-rich repeat (ISLR) gene in gastric cancer and examine whether ISLR could interact with N-acetylglucosaminyltransferase V (MGAT5) to affect the malignant progression of gastric cancer.

Materials and Methods

The expression of ISLR and MGAT5 in human normal gastric epithelial cells and human gastric cancer cells, and the transfection efficiency of ISLR interference plasmids and MGAT5 overexpression plasmids were all detected by reverse transcription-quantitative PCR (RT-qPCR) and western blot. The viability, proliferation, migration and invasion, and epithelial-mesenchymal transition (EMT) of gastric cancer cells after indicated transfection were detected by Cell counting kit-8 (CCK-8) assay, 5-ethynyl-2'-deoxyuridine (EdU) staining, wound healing assay, and transwell assay. The interaction between ISLR and MGAT5 was confirmed by co-immunoprecipitation. The expression of proteins related to migration, invasion, and EMT was detected by immunofluorescence and western blot.

Results

As a result, ISLR was highly expressed in gastric cancer and was associated with poor prognosis. Interference with ISLR inhibited the viability, proliferation, migration, invasion, and EMT of gastric cancer cells. ISLR interacted with MGAT5 in gastric cancer cells. MGAT5 overexpression weakened the effects of ISLR knockdown on suppressing the viability, proliferation, migration, invasion, and EMT of gastric cancer cells.

Conclusion

ISLR interacted with MGAT5 to promote the malignant progression of gastric cancer.

Silencing of immunoglobulin superfamily containing leucine-rich repeat inhibits gastric cancer cell growth and metastasis by regulating epithelial-mesenchymal transition

This study aims to investigate the immunoglobulin superfamily containing leucine-rich repeat (ISLR) expression in gastric cancer (GC) and ISLR’s underlying mechanisms regulation of GC progression. Through The Cancer Genome Atlas (TCGA) cohort datasets, we analyzed the ISLR expression in GC tumor tissues and normal tissues. ISLR expression in GC tissues and cells was determined using quantitative real-time polymerase chain reaction. Cell viability, proliferation, migration, and invasion assays were performed in GC cells transfected with sh-ISLR, ISLR plasmids, or controls. TCGA results showed that ISLR expression was higher in GC tumor tissues compared to normal tissues, and its expression levels were related to lymph node metastasis, tumor size, and clinical stage. ISLR was highly expressed in tumor cells. ISLR knockdown suppressed cell viability, proliferation, migration, and invasion in HGC-27 cells, whereas ISLR overexpression led to opposite effects in AGS cells. Gene Set Enrichment Analysis showed that ISLR could activate the epithelial–mesenchymal transition (EMT) signaling pathway. Silencing of ISLR suppressed EMT in HGC-27 cells and overexpression of ISLR promoted EMT in AGS cells. ISLR was overexpressed in both GC cell lines and tumor tissues, and our study first showed that silencing of ISLR inhibited GC cell growth and metastasis by reversing EMT.

ISLR affects colon cancer progression by regulating the epithelial-mesenchymal transition signaling pathway

This study aims to determine the mechanism of ISLR on the progression of colon cancer. TCGA database was used to analyze ISLR expression in colon cancer tumor tissues. QRT-PCR and western blotting were used to detect ISLR expression in colon cancer cells. CCK-8, colony formation, EDU, wound healing and transwell assays were used to measure cell viability, proliferation, migration and invasion of colon cancer cells, respectively. The signaling pathway enrichment analysis of ISLR was analyzed on the basis of the KEGG database. The protein expression of genes related to signaling pathway was measured by western blotting. Results of TCGA analysis, qRT-PC and western blotting showed that ISLR was upregulated in colon cancer tumor tissues and cells. High level of ISLR was related to low overall survival of patients with colon cancer. ISLR silence significantly inhibited cell viability, proliferation, migration and invasion of colon cancer cells. ISLR overexpression markedly enhanced the cell viability, proliferation, migration and invasion of colon cancer cells. KEGG database analyzed showed that ISLR can activate the EMT signaling pathway. Inhibition of the EMT signaling pathway can suppress the growth, migration, and invasion of colon cancer cells and eliminate the promoted effect of ISLR overexpression on colon cancer progression. ISLR promotes the progression of colon cancer by activating the EMT signaling pathway.

Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

Overexpression of NREP Promotes Migration and Invasion in Gastric Cancer Through Facilitating Epithelial-Mesenchymal Transition

The identification of biomarkers and effective therapeutic targets for gastric cancer (GC), the most common cause of cancer-related deaths around the world, is currently a major focus area in research. Here, we examined the utility of Neuronal Regeneration Related Protein (NREP) as a prognostic biomarker and therapeutic target for GC. We assessed the clinical relevance, function, and molecular role of NREP in GC using bioinformatics analysis and experimental validation. Our results showed that in GC, NREP overexpression was significantly associated with a poor prognosis. Our findings also suggested that NREP may be involved in the activation of cancer-associated fibroblasts and the epithelial-mesenchymal transition (EMT), with transforming growth factor β1 mediating both processes. In addition, NREP expression showed a positive correlation with the abundance of M2 macrophages, which are potent immunosuppressors. Together, these results indicate that NREP is overexpressed in GC and affects GC prognosis. Thus, NREP could be a prognostic biomarker and therapeutic target for GC.

Silencing of ISLR inhibits tumour progression and glycolysis by inactivating the IL‑6/JAK/STAT3 pathway in non‑small cell lung cancer

Lung cancer is the second most frequent cancer type in both men and women, and it is considered to be one of the major causes of cancer-related mortality worldwide. However, few biomarkers are currently available for the diagnosis of lung cancer. The aim of the present study was to investigate the function of the immunoglobulin superfamily containing leucine-rich repeat (ISLR) gene in non-small cell lung cancer (NSCLC) cells, and to elucidate the underlying molecular mechanism of its action. The current study analysed ISLR expression in NSCLC tumour and normal tissues using The Cancer Genome Atlas cohort datasets. ISLR expression in NSCLC cell lines was determined using reverse transcription-quantitative PCR. Cell Counting Kit-8, soft agar colony formation, wound healing, Transwell, flow cytometry and glycolysis assays were performed to determine the effects of ISLR silencing or overexpression on cells. The expression levels of the genes involved in epithelial-mesenchymal transition (EMT), apoptosis and glycolysis were evaluated via western blotting. Transfected cells were exposed to the pathway activator, IL-6, to validate the regulatory pathway. ISLR was overexpressed in NSCLC tissues and cell lines. Overall, patients with high ISLR expression had lower survival rates. In addition, small interfering RNA-ISLR inhibited the proliferation, EMT, migration, invasion and glycolysis of NSCLC cells, and promoted their apoptosis. ISLR overexpression had the opposite effect on tumour progression and glycolysis in NSCLC cells. Gene set enrichment analysis and western blotting results indicated that the IL-6/Janus kinase (JAK)/STAT3 pathway was enriched in ISLR-related NSCLC. Knockdown of ISLR inhibited IL-6-induced proliferation, invasion, migration and glycolysis in human NSCLC cells. In summary, ISLR silencing can inhibit tumour progression and glycolysis in NSCLC cells by activating the IL-6/JAK/STAT3 signalling pathway, which is a potential molecular target for NSCLC diagnosis and treatment.

Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients

BACKGROUND

The clinical pathologic stages (stage I, II, III-IV) of hepatocellular carcinoma (HCC) are closely linked to the clinical prognosis of patients. This study aims at investigating the gene expression and mutational profile in different clinical pathologic stages of HCC.

METHODS

Based on the TCGA-LIHC cohort, we utilized a series of analytical approaches, such as statistical analysis, random forest, decision tree, principal component analysis (PCA), to identify the differential gene expression and mutational profiles. The expression patterns of several targeting genes were also verified by analyzing the Chinese HLivH060PG02 HCC cohort, several GEO datasets, HPA database, and diethylnitrosamine-induced HCC mouse model.

RESULTS

We identified a series of targeting genes with copy number variation, which is statistically associated with gene expression. Non-synonymous mutations mainly existed in some genes (e.g.,TTN, TP53, CTNNB1). Nevertheless, no association between gene mutation frequency and pathologic stage distribution was detected. The random forest and decision tree modeling analysis data showed a group of genes related to different HCC pathologic stages, including GAS2L3 and SEMA3F. Additionally, our PCA data indicated several genes associated with different pathologic stages, including SNRPA and SNRPD2. Compared with adjacent normal tissues, we observed a highly expressed level of GAS2L3, SNRPA, and SNRPD2 (P = 0.002) genes in HCC tissues of our HLivH060PG02 cohort. We also detected the high expression pattern of GAS2L3, SEMA3F, SNRPA, and SNRPD2 in the datasets of GSE102079, GSE76427, GSE64041, GSE121248, GSE84005, and the qPCR assay using diethylnitrosamine-induced HCC mouse model. Moreover, SEMA3F and SNRPD2 protein were highly stained in the HCC tissues of the HPA database. The high expression level of these four genes was associated with the poor survival prognosis of HCC cases.

CONCLUSIONS

Our study provides evidence regarding the gene expression and mutational profile in different clinical pathologic stages of TCGA HCC cases. Identifying four targeting genes, including GAS2L3, SNRPA, SNRPD2, and SEMA3F, offers insight into the molecular mechanisms associated with different prognoses of HCC.

Investigation on Potential Correlation Between Small Nuclear Ribonucleoprotein Polypeptide A and Lung Cancer

SNRPA (small nuclear ribonucleoprotein polypeptide A) gene is essential for the pre-mRNA splicing process. Using the available datasets of TCGA or GEO, we aimed at exploring the potential association between the SNRPA gene and lung cancer by several online tools (such as GEIPA2, MEXPRESS, Oncomine) and bioinformatics analysis software (R or GSEA). SNRPA was highly expressed in the tissues of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma tissue (LUSC), compared with control tissues. The high SNRPA expression was associated with a poor survival prognosis of LUAD cases, while the genetic alteration within SNRPA was linked to the overall survival prognosis of LUSC cases. There was a potential correlation between promoter methylation and the expression of SNRPA for LUAD. Compared with normal tissues, we observed a higher phosphorylation level at the S115 site of SNRPA protein (NP_004587.1) (p = 0.002) in the primary LUAD tissues. The potential ATR kinase of the S115 site was predicted. Besides, SNRPA expression in lung cancer was negatively correlated with the infiltration level of M2 macrophage but positively correlated with that of Follicular B helper T cells, in both LUAD and LUSC. The enrichment analysis of SNRPA-correlated genes showed that cell cycle and ubiquitin mechanism-related issues were mainly observed for LUAD; however, RNA splicing-related cellular issues were mainly for LUSC. In summary, the SNRPA gene was identified as a potential prognosis biomarker of lung cancer, especially lung adenocarcinoma, which sheds new light on the association between the spliceosomal complex component and tumorigenesis.

Cancer-associated fibroblasts and their influence on tumor immunity and immunotherapy

Fibroblasts play an essential role in organogenesis and the integrity of tissue architecture and function. Growth in most solid tumors is dependent upon remodeling 'stroma', composed of cancer-associated fibroblasts (CAFs) and extracellular matrix (ECM), which plays a critical role in tumor initiation, progression, metastasis, and therapeutic resistance. Recent studies have clearly established that the potent immunosuppressive activity of stroma is a major mechanism by which stroma can promote tumor progression and confer resistance to immune-based therapies. Herein, we review recent advances in identifying the stroma-dependent mechanisms that regulate cancer-associated inflammation and antitumor immunity, in particular, the interactions between fibroblasts and immune cells. We also review the potential mechanisms by which stroma can confer resistance to immune-based therapies for solid tumors and current advancements in stroma-targeted therapies.