Genome-Scale Analysis Identified NID2, SPARC, and MFAP2 as Prognosis Markers of Overall Survival in Gastric Cancer

Curcumin inhibits the proliferation and migration of osteosarcoma by regulating the expression of superenhancerassociated genes

OBJECTIVES

Super-enhancer-associated genes may be closely related to the progression of osteosarcoma, curcumin exhibits a certain inhibitory effect on tumors such as osteosarcoma. This study aims to investigate the effects of curcumin on osteosarcoma in vitro and in vivo, and to determine whether curcumin can inhibit the progression of osteosarcoma by suppressing the expression of super-enhancer-associated genes LIM and senescent cell antigen-like-containing domain 1 (LIMS1), secreted protein acidic and rich in cysteine (SPARC), and sterile alpha motif domain containing 4A (SAMD4A).

METHODS

Human osteosarcoma cell lines (MG63 cells or U2OS cells) were treated with 5 to 50 μmol/L curcumin for 24, 48, and 72 hours, followed by the methyl thiazolyl tetrazolium (MTT) assay to detect cell viability. Cells were incubated with dimethyl sulfoxide (DMSO) or curcumin (2.5, 5.0 μmol/L) for 7 days, and a colony formation assay was used to measure in vitro cell proliferation. After treatment with DMSO or curcumin (10, 15 μmol/L), a scratch healing assay and a transwell migration assay were performed to evaluate cell migration ability. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) and Western blotting were used to detect mRNA and protein expression levels of LIMS1, SPARC, and SAMD4A in the cells. An osteosarcoma-bearing nude mouse model was established, and curcumin was administered via gavage for 14 days to assess the impact of curcumin on tumor volume and weight in vivo. Real-time RT-PCR was used to measure mRNA expression levels of LIMS1, SPARC, and SAMD4A in the cancer and adjacent tissues from 12 osteosarcoma patients.

RESULTS

After treating cells with different concentrations of curcumin for 24, 48, and 72 hours, cell viability were all significantly decreased. Compared with the DMSO group, the colony formation rates in the 2.5 μmol/L and 5.0 μmol/L curcumin groups significantly declined (both P<0.01). The scratch healing assay showed that, compared with the DMSO group, the migration rates of cells in the 10 μmol/L and 15 μmol/L curcumin groups were significantly reduced. The exception was the 10 μmol/L curcumin group at 24 h, where the migration rate of U2OS cells did not show a statistically significant difference (P>0.05), while all other differences were statistically significant (P<0.01 or P<0.001). The transwell migration assay results showed that the number of migrating cells in the 10 μmol/L and 15 μmol/L curcumin groups was significantly lower than that in the DMSO group (both P<0.001). In the in vivo tumor-bearing mouse experiment, the curcumin group showed a reduction in tumor mass (P<0.01) and a significant reduction in tumor volume (P<0.001) compared with the control group. Compared with the DMSO group, the mRNA expression levels of LIMS1, SPARC, and SAMD4A in the 10 μmol/L and 15 μmol/L curcumin groups were significantly down-regulated (all P<0.05). Additionally, the protein expression level of LIMS1 in U2OS cells in the 10 μmol/L curcumin group was significantly lower than that in the DMSO group (P<0.05). Compared with adjacent tissues, the mRNA expression level of SPARC in osteosarcoma tissues was significantly increased (P<0.001), while the mRNA expression levels of LIMS1 and SAMD4A did not show statistically significant differences (both P>0.05).

CONCLUSIONS

Curcumin inhibits the proliferation and migration of osteosarcoma both in vitro and in vivo, which may be associated with the inactivation of super-enhancer-associated gene LIMS1.

SPARC: a potential target for functional nanomaterials and drugs

Secreted protein acidic and rich in cysteine (SPARC), also termed osteonectin or BM-40, is a matricellular protein which regulates cell adhesion, extracellular matrix production, growth factor activity, and cell cycle. Although SPARC does not perform a structural function, it, however, modulates interactions between cells and the surrounding extracellular matrix due to its anti-proliferative and anti-adhesion properties. The overexpression of SPARC at sites, including injury, regeneration, obesity, cancer, and inflammation, reveals its application as a prospective target and therapeutic indicator in the treatment and assessment of disease. This article comprehensively summarizes the mechanism of SPARC overexpression in inflammation and tumors as well as the latest research progress of functional nanomaterials in the therapy of rheumatoid arthritis and tumors by manipulating SPARC as a new target. This article provides ideas for using functional nanomaterials to treat inflammatory diseases through the SPARC target. The purpose of this article is to provide a reference for ongoing disease research based on SPARC-targeted therapy.

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.

Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks

Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein–protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases.

Nidogen-2 (NID2) is a Key Factor in Collagen Causing Poor Response to Immunotherapy in Melanoma

Background

The incidence of cutaneous melanoma continues to rise rapidly and has an extremely poor prognosis. Immunotherapy strategies are the most effective approach for patients who have developed metastases, but not all cases have been successful due to the complex and variable mechanisms of melanoma response to immune checkpoint inhibition.

Methods

We synthesized collagen-coding gene expression data (second-generation and single-cell sequencing) from public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatics analysis was performed using R software and several database resources such as Metascape database, Gene Set Cancer Analysis (GSCA) database, and Cytoscape software, etc., to investigate the biological mechanisms that may be related with collagens. Immunofluorescence and immunohistochemical staining were used to validate the expression and localization of Nidogen-2 (NID2).

Results

Melanoma patients can be divided into two collagen clusters. Patients with high collagen levels (C1) had a shorter survival than those with low collagen levels (C2) and were less likely to benefit from immunotherapy. We demonstrated that NID2 is a potential key factor in the collagen phenotype, is involved in fibroblast activation in melanoma, and forms a barrier to limit the proximity of CD8+ T cells to tumor cells.

Conclusion

We clarified the adverse effects of collagen on melanoma patients and identified NID2 as a potential therapeutic target.

Comprehensive analysis of tissue proteomics in patients with papillary thyroid microcarcinoma uncovers the underlying mechanism of lymph node metastasis and its significant sex disparities

Background

Lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC) is associated with an increased risk of recurrence and poor prognosis. Sex has been regarded as a critical risk factor for LNM. The present study aimed to investigate the molecular mechanisms underlying LNM and its significant sex disparities in PTMC development.

Methods

A direct data-independent acquisition (DIA) proteomics approach was used to identify differentially expressed proteins (DEPs) in PTMC tumorous tissues with or without LNM and from male and female patients with LNM. The functional annotation of DEPs was performed using bioinformatics methods. Furthermore, The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) dataset and immunohistochemistry (IHC) were used to validate selected DEPs.

Results

The proteomics profile in PTMC with LNM differed from that of PTMC without LNM. The metastasis-related DEPs were primarily enriched in categories associated with mitochondrial dysfunction and may promote tumor progression by activating oxidative phosphorylation and PI3K/AKT signaling pathways. Comparative analyses of these DEPs revealed downregulated expression of specific proteins with well-established links to tumor metastasis, such as SLC25A15, DIRAS2, PLA2R1, and MTARC1. Additionally, the proteomics profiles of male and female PTMC patients with LNM were dramatically distinguishable. An elevated level of ECM-associated proteins might be related to more LNM in male PTMC than in female PTMC patients. The upregulated expression levels of MMRN2 and NID2 correlated with sex disparities and showed a positive relationship with unfavorable variables, such as LNMs and poor prognosis.

Conclusions

The proteomics profiles of PTMC show significant differences associated with LNM and its sex disparities, which further expands our understanding of the functional networks and signaling pathways related to PTMC with LNM.

MFAP2 aggravates tumor progression through activating FOXM1/β-catenin-mediated glycolysis in ovarian cancer

Ovarian cancer is one of the most common gynecological tumors that seriously endanger the health and quality of life of women. Microfibril-associated protein 2 (MFAP2) has been demonstrated to play crucial roles in the development of multiple tumors. However, the function of MFAP2 in ovarian cancer remains unclear. In this study, we found that MFAP2 was upregulated in ovarian cancer and cells and was positively correlated with FOXM1 and glycolysis-related genes. The results of Cell Count Kit-8, colony formation, and flow cytometry assays indicated that MFAP2 promoted cell proliferation. In addition, MFAP2 promotes cell proliferation, glucose uptake, lactate production; increases ATP levels, extracellular acidification ratio, and oxygen consumption ratio in ovarian cancer cells and increases the expression of glycolytic proteins. Further mechanistic analysis suggests that MFAP2 promotes FOXM1/β-catenin-mediated glycolysis signaling in ovarian cancer cells. Knockdown of MFAP2 inhibits ovarian cancer xenograft tumor growth and expression of Ki-67, MFAP2, FOXM1, GLUT1, HK2, and β-catenin in mice. In conclusion, MFAP2 promotes cell proliferation and glycolysis by modulating the FOXM1/β-catenin signaling pathway in ovarian cancer, which may offer a fresh insight into the treatment of ovarian cancer in the glycolysis pathway.

Pan-Cancer Analysis of Microfibrillar-Associated Protein 2 (MFAP2) Based on Bioinformatics and qPCR Verification

MFAP2 has been reported to play an oncogenic role in several types of human cancers. However, the expression profile of MFAP2 in various cancers and its impact on prognosis and immune infiltration remain unclear. In this study, the mRNA expression and protein expression of MFAP2 in normal tissues, tumor cell lines, and 33 malignant tumor tissues were analyzed comprehensively using Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA), Oncomine and UALCAN databases, and the expression of MFAP2 in different grades and stages of cancers was assessed using Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and Tumor and Immune System Interaction Database (TISIDB). In general, MFAP2 showed distinct expression in most tumor and normal tissues, closely associated with higher tumor grade, higher tumor stage, and poor survival in multiple cancers. A search of the UALCAN database and the cBioPortal database revealed that this difference in mRNA level expression could be partly attributed to abnormal DNA methylation and mutations at the genomic level. In addition, MFAP2 expression was also associated with tumor mutation burden, microsatellite instability, and neoantigens in different cancer types. More importantly, the TIMER and TISIDB databases also showed that MFAP2 levels were significantly correlated with immune infiltration abundance and immune-related gene markers, as well as ESTIMATE scores. By qPCR, MFAP2 expression was validated in four kinds of tumor tissue samples. The present study combined several databases and performed a pan-cancer analysis of the expression profile, methylation, and mutation for MFAP2 and its implications for prognosis and immune infiltration, suggesting that MFAP2 could contribute to malignant properties of many tumors. MFAP2 may be an important biomarker with prognostic value and has the potential to be a target for tumor immunotherapy.

DNA Methylation Mediates EMT Gene Expression in Human Pancreatic Ductal Adenocarcinoma Cell Lines

Due to abundant stroma and extracellular matrix, accompanied by lack of vascularization, pancreatic ductal adenocarcinoma (PDAC) is characterized by severe hypoxia. Epigenetic regulation is likely one of the mechanisms driving hypoxia-induced epithelial-to-mesenchymal transition (EMT), responsible for PDAC aggressiveness and dismal prognosis. To verify the role of DNA methylation in this process, we assessed gene expression and DNA methylation changes in four PDAC cell lines. BxPC-3, MIA PaCa-2, PANC-1, and SU.86.86 cells were exposed to conditioned media containing cytokines and inflammatory molecules in normoxic and hypoxic (1% O2) conditions for 2 and 6 days. Cancer Inflammation and Immunity Crosstalk and Human Epithelial to Mesenchymal Transition RT² Profiler PCR Arrays were used to identify top deregulated inflammatory and EMT-related genes. Their mRNA expression and DNA methylation were quantified by qRT-PCR and pyrosequencing. BxPC-3 and SU.86.86 cell lines were the most sensitive to hypoxia and inflammation. Although the methylation of gene promoters correlated with gene expression negatively, it was not significantly influenced by experimental conditions. However, DNA methyltransferase inhibitor decitabine efficiently decreased DNA methylation up to 53% and reactivated all silenced genes. These results confirm the role of DNA methylation in EMT-related gene regulation and uncover possible new targets involved in PDAC progression.

Long non-coding RNA SNHG1 promotes bladder cancer progression by upregulating EZH2 and repressing KLF2 transcription

OBJECTIVE

Long Non-Coding RNAs (LncRNAs) act as an indispensable role in cancer development. The study aimed to investigate the role and mechanism of lncRNA Small Nucleolar RNA Host Gene 1 (SNHG1) in Bladder Cancer (BC) progression.

METHOD

The expression, prognostic value, diagnostic value, and correlation of SNHG1, Enhancer of Zeste 2 polycomb repressive complex 2 subunit (EZH2), and Kruppel Like Factor 2 (KLF2) were analyzed through bioinformatics analysis. The expression was also validated in BC tissues and cell lines. Besides, their regulation and binding were tested via qPCR, Western blot, Dual-Luciferase Reporter Assay (DLRA), Argonaute RISC catalytic component 2-RNA Immunoprecipitation (AGO2-RIP), and Chromatin Immunoprecipitation (ChIP). A xenograft model in nude mice was also established.

RESULTS

SNHG1 was significantly overexpressed in BC tissues and cells. Importantly, SNHG1 was associated with poor survival, and ROC curves revealed high diagnostic values. Moreover, by CCK8, wound healing, transwell, and Western blot analysis, SNHG1 knockdown significantly inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition of BC cells. Additionally, in vivo experiments showed that silencing SNHG1 hindered tumorigenesis and tumor growth. Regarding mechanism, the results of AGO2-RIP, ChIP or DLRA showed that SNHG1 played different roles at diverse subcellular sites. In the cytoplasm, SNHG1 acted as a competing endogenous RNA for miR-137-3p to promote EZH2 expression. In the nucleus, SNHG1 could interact with EZH2 to inhibit KLF2 transcription.

CONCLUSION

Our study elucidated that SNHG1 formed a regulatory network and played an oncogenic role in BC, which provided a novel therapeutic target for BC treatment.

Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.