Bioinformatics Analysis Identifies a Novel Role of GINS1 Gene in Colorectal Cancer

GINS1 promotes ZEB1-mediated epithelial-mesenchymal transition and tumor metastasis via β-catenin signaling in hepatocellular carcinoma

GINS1 regulates DNA replication in the initiation and elongation phases and plays an important role in the progression of various malignant tumors. However, the role of GINS1 in hepatocellular carcinoma (HCC) remains largely unclear. In this study, we investigated the role and underlying mechanisms of GINS1 in contributing to HCC metastasis. We found that GINS1 was significantly upregulated in HCC tissues and cell lines, especially in HCC tissues with vascular invasion and HCC cell lines with highly metastatic properties. Additionally, high expression of GINS1 was positively correlated with the progressive clinical features of HCC patients, including tumor number (multiple), tumor size (>5 cm), advanced tumor stage, vascular invasion and early recurrence, suggesting that GINS1 upregulation was greatly involved in HCC metastasis. Moreover, Kaplan-Meier survival analysis revealed that high GINS1 expression predicted a poor prognosis. Both in vitro and in vivo, silencing of GINS1 inhibited proliferation, migration, invasion and metastasis, while overexpression of GINS1 induced opposite effects. Mechanistically, we found that ZEB1 was a crucial regulator of GINS1-induced epithelial-mesenchymal transition (EMT), and GINS1 promoted EMT and tumor metastasis through β-catenin signaling. Overall, the present study demonstrated that GINS1 promoted ZEB1-mediated EMT and tumor metastasis via β-catenin signaling in HCC.

Genetic background of juniper (Juniperus spp.) consumption predicted by fecal near-infrared spectroscopy in divergently selected goats raised in harsh rangeland environments

BACKGROUND

Junipers (Juniperus spp.) are woody native, invasive plants that have caused encroachment problems in the U.S. western rangelands, decreasing forage productivity and biodiversity. A potential solution to this issue is using goats in targeted grazing programs. However, junipers, which grow in dry and harsh environmental conditions, use chemical defense mechanisms to deter herbivores. Therefore, genetically selecting goats for increased juniper consumption is of great interest for regenerative rangeland management. In this context, the primary objectives of this study were to: 1) estimate variance components and genetic parameters for predicted juniper consumption in divergently selected Angora (ANG) and composite Boer x Spanish (BS) goat populations grazing on Western U.S. rangelands; and 2) to identify genomic regions, candidate genes, and biological pathways associated with juniper consumption in these goat populations.

RESULTS

The average juniper consumption was 22.4% (± 18.7%) and 7.01% (± 12.1%) in the BS and ANG populations, respectively. The heritability estimates (realized heritability within parenthesis) for juniper consumption were 0.43 ± 0.02 (0.34 ± 0.06) and 0.19 ± 0.03 (0.13 ± 0.03) in BS and ANG, respectively, indicating that juniper consumption can be increased through genetic selection. The repeatability values of predicted juniper consumption were 0.45 for BS and 0.28 for ANG. A total of 571 significant SNP located within or close to 231 genes in BS, and 116 SNP related to 183 genes in ANG were identified based on the genome-wide association analyses. These genes are primarily associated with biological pathways and gene ontology terms related to olfactory receptors, intestinal absorption, and immunity response.

CONCLUSIONS

These findings suggest that juniper consumption is a heritable trait of polygenic inheritance influenced by multiple genes of small effects. The genetic parameters calculated indicate that juniper consumption can be genetically improved in both goat populations.

Transcription Factor E2F1 Enhances Hepatocellular Carcinoma Cell Proliferation and Stemness by Activating GINS1

Present studies report that high expression of GINS complex subunit 1 (GINS1) is notably pertinent to poor survival for hepatocellular carcinoma (HCC), but it remains unclear how GINS1 affects the progression of HCC. This study aims at investigating the mechanism by which GINS1 affects HCC cell proliferation and stemness. We performed bioinformatics analysis for determining GINS1 expression in HCC tissues, as well as the HCC patients' survival rate with different expression levels of GINS1. E2F transcription factor 1 (E2F1) was predicted as the upstream transcription factor of GINS1, and the binding relation between the two was verified by chromatin immunoprecipitation and dual-luciferase reporter assays. Quantitative real-time polymerase chain reaction was adopted to evaluate the expression of GINS1 and E2F1. The protein expression levels of GINS1, E2F1, and cell stemness-related genes (SOX-2, NANOG, OCT4, and CD133) were detected by Western blot. Afterward, the proliferative capacity and stemness of HCC tumor cells were determined through colony formation, cell counting kit-8, and sphere formation assays. Our study found the high expression of GINS1 and E2F1 in HCC, and overexpressed GINS1 markedly enhanced the sphere formation and proliferation of HCC cells, while silencing GINS1 led to the opposite results. Besides, E2F1 promoted the transcription of GINS1 by working as an upstream transcription factor. The results of the rescue experiment suggested that overexpressed E2F1 could offset the suppressive effect of GINS1 silencing on HCC cell stemness and proliferation. We demonstrated that the transcription factor E2F1 accelerated cell proliferation and stemness in HCC by activating GINS1 transcription. The results can provide new insight into the GINS1-related regulatory mechanism in HCC, which suggest that it may be an effective way for HCC treatment by targeting the E2F1/GINS1 axis.

Roles of DSCC1 and GINS1 in gastric cancer

Gastric carcinoma is a common malignant tumor originating from gastric mucosal epithelium. However, role of DS-cell cycle-dependent protein 1 (DSCC1) and GINS1 in gastric carcinoma remains unclear. The gastric carcinoma datasets GSE79973 and GSE118916 were downloaded from gene expression omnibus. Multiple datasets were merged and batched. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. Functional enrichment analysis, gene set enrichment analysis and immune infiltration analysis were performed. Construction and analysis of protein-protein interaction Network. Survival analysis and comparative toxicogenomics database were performed. A heat map of gene expression was drawn. Target Scan screen miRNAs regulating DEGs. Two thousand forty-four DEGs were identified. According to gene ontology analysis, in biological process, they were mainly enriched in cell migration, transforming growth factor β receptor signaling pathway, angiogenesis, and steroid metabolism process. In cellular component, they were mainly enriched in extracellular vesicles, basement membrane, endoplasmic reticulum lumen, and extracellular space. In molecular function, they focused on extracellular matrix structural components, protein binding, platelet-derived growth factor binding, and catalytic activity. In Kyoto encyclopedia of genes and genomes, they were mainly enriched in protein digestion and absorption, metabolic pathways, fatty acid degradation, Glycerophospholipid metabolism, ether lipid metabolism. Gene set enrichment analysis showed that DEGs were mainly enriched in transforming growth factor β receptor signaling pathway, steroid metabolism process, basement membrane, endoplasmic reticulum lumen, structural components of extracellular matrix, platelet-derived growth factor binding, Glycerophospholipid metabolism, ether lipid metabolism. The results of immune infiltration analysis showed that expression of T cell CD4 memory resting was lower in the samples of gastric cancer. The core genes (TRIP13, CHEK1, DSCC1, GINS1) are protective factors, their expression shows a downward trend with increase of risk score. Comparative toxicogenomics database analysis showed that TRIP13, CHEK1, DSCC1, GINS1 were related to gastric tumors, gastric diseases, tumors, inflammation, and necrosis. DSCC1 and GINS1 are highly expressed in gastric cancer. Higher expression levels of DSCC1 and GINS1, worse the prognosis.

Identification of autophagy and angiogenesis modulators in colorectal cancer based on bioinformatics analysis

Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer-related death worldwide. The purpose of this study was to discover novel molecular pathways and potential prognosis biomarkers. To achieve this, we acquired five microarray datasets from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes between CRC and adjacent normal tissue samples and further validated them using The Cancer Genome Atlas (TCGA) database. Using various analytical approaches, including the construction of a competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as survival analysis, we identified key genes and pathways associated with the diagnosis and prognosis of CRC. We obtained a total of 185 differentially expressed genes, comprising 17 lncRNAs, 30 miRNAs, and 138 mRNAs. The ceRNA network consisted of 17 lncRNAs, 25 miRNAs, and 7 mRNAs. Among the 7 mRNAs involved in the ceRNA network, SLC7A5 and KRT80 were found to be upregulated, while ADIPOQ, CCBE1, KCNB1, CADM2, and CHRDL1 were downregulated in CRC. Further analysis revealed that ADIPOQ and SLC7A5 are involved in the AMPK and mTOR signaling pathway, respectively. In addition, survival analysis demonstrated a statistically significant relationship between ADIPOQ, SLC7A5, and overall survival rates in CRC patients. In conclusion, our findings suggest that downregulation of ADIPOQ and upregulation of SLC7A5 in tumor cells lead to increased mTORC1 activity, reduced autophagy, enhanced angiogenesis, and ultimately contribute to cancer progression and decreased survival in CRC patients.

Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis

BACKGROUND

The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease.

METHODS

We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes.

RESULTS

In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2.

CONCLUSION

Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.

A Novel Tumor-Promoting Role for Nuclear Factor IX in Glioblastoma Is Mediated through Transcriptional Activation of GINS1

Our previous study illustrated that nuclear factor IX (NFIX) promotes glioblastoma (GBM) progression by inducing migration and proliferation of GBM cells. However, the underlying mechanism of how NFIX regulates GBM cell proliferation remains obscure. In this study, we uncovered that Go-Ichi-Ni-San 1 (GINS1) is upregulated and positively correlated with NFIX in human GBM specimen. NFIX silencing downregulates the expression of GINS1, which is pivotal for cell-cycle progression and proliferation of GBM cells. Replenishment of GINS1 largely rescues the NFIX-null effect on GBM cell proliferation. Mechanistic investigation revealed that NFIX transcriptionally actives GINS1 expression by directly binding to promoter region (-1779 to -1793bp) of the GINS1 gene. Furthermore, knockdown of NFIX sensitizes GBM cells to DNA damage-inducing agents including doxorubicin and temozolomide, in a GINS1-dependent manner.

IMPLICATIONS

Our study highlights that targeting NFIX-GINS1 axis could be a novel and potential therapeutic approach for GBM treatment.

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Background

Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify transcriptome differences between CRA and CRC via deep learning on Gene Expression Omnibus (GEO) databases and bioinformatics in the Chinese population.

Methods

In this study, three microarray datasets from the GEO database were used to identify the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in CRA and CRC. The FunRich software was performed to predict the targeted mRNAs of DEMs. The targeted mRNAs were overlapped with DEGs to determine the key DEGs. Molecular mechanisms of CRA and CRC were evaluated using enrichment analysis. Cytoscape was used to construct protein-protein interaction (PPI) and miRNA-mRNA regulatory networks. We analyzed the expression of key DEMs and DEGs, their prognosis, and correlation with immune infiltration based on the Kaplan-Meier plotter, UALCAN, and TIMER databases.

Results

A total of 38 DEGs are obtained after the intersection, including 11 upregulated genes and 27 downregulated genes. The DEGs were involved in the pathways, including epithelial-to-mesenchymal transition, sphingolipid metabolism, and intrinsic pathway for apoptosis. The expression of has-miR-34c (P = 0.036), hsa-miR-320a (P = 0.045), and has-miR-338 (P = 0.0063) was correlated with the prognosis of CRC patients. The expression levels of BCL2, PPM1L, ARHGAP44, and PRKACB in CRC tissues were significantly lower than normal tissues (P < 0.001), while the expression levels of TPD52L2 and WNK4 in CRC tissues were significantly higher than normal tissues (P < 0.01). These key genes are significantly associated with the immune infiltration of CRC.

Conclusion

This preliminary study will help identify patients with CRA and early CRC and establish prevention and monitoring strategies to reduce the incidence of CRC.

GINS1 promotes the proliferation and migration of glioma cells through USP15-mediated deubiquitination of TOP2A

GINS1 is a GINS complex subunit that functions along with the MCM2-7 complex and Cdc45 in eukaryotic DNA replication. Despite the significance of the GINS complex in the switch between quiescence and proliferation of glioma cells inside and outside the perinecrotic niche, the biological functions and the underlying mechanism of GINS1 remain unclear. Unlike in normal cells and tissues, GINS1 expression level was significantly upregulated in glioma cells and tissues. High expression of GINS1 predicted an advanced clinical grade and a poor survival. Functional assays revealed that GINS1 aggravated glioma malignant phenotypes in vitro and in vivo. Mechanistically, this study identified that GINS1 physically interacts with TOP2A. GINS1 promotes glioma cell proliferation and migration through USP15-mediated deubiquitination of TOP2A protein. Our results delineate the clinical significance of GINS1 in glioma and the regulatory mechanisms involved in glioma cell proliferation and migration. This work provides potential therapeutic targets for glioma treatment.

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GINS1 expression was upregulated and associated with poor clinical outcome in glioma

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GINS1 aggravated glioma malignant phenotypes in vitro and in vivo

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GINS1 physically interacts with TOP2A

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GINS1 regulates glioma progression through USP15-mediated deubiquitination of TOP2A

Combined analysis of expression, prognosis and immune infiltration of GINS family genes in human sarcoma

Objective: This study was undertaken to explore the expression and prognostic value of GINS family in human sarcoma, as well as the association between the expression levels of the GINS family and sarcoma immune infiltration.

Results: We discovered that the mRNA expression levels of GINS1, GINS2, GINS3, and GINS4 were all higher in the majority of tumor tissues than in normal samples, of course, including sarcoma. Through the CCLE, all the four members expression were observed in high levels in sarcoma cell lines. In Gene Expression Profiling Analysis (GEPIA) and Kaplan-Meier Plotter, our results indicated that the poor overall survival (OS), disease-free survival (DFS) and relapse free survival (RFS) were tightly associated with the increased expression of GINS genes. In TIMER database, we found that highly expressed GINS was significantly correlated with the low infiltration level of CD4+ T cell and macrophage.

Conclusions: The four GINS family members were all the prognostic biomarkers for the prognosis of human sarcoma and can reduce the level of immune cell infiltration in the sarcoma microenvironment.

Methods: In terms of the expression levels of mRNA for GINS family members, a particular contrast in various cancers, especially human sarcoma, was conducted through ONCOMINE and GEPIA and CCLE databases. Kaplan-Meier Plotter was used to identify the prognostic value of GINS family in sarcoma. The relationship between the expression level of GINS and the infiltration of immune cells was analyzed in TIMER database.

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis.

Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively.

Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value.

Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

GINS Complex Subunit 2 Facilitates Gastric Adenocarcinoma Proliferation and Indicates Poor Prognosis

Gastric cancer is the one of the most lethal malignancies of digestive system. Identifying molecular biomarkers is invaluable in help predicting clinical outcomes and developing targeted chemotherapies. GINS complex subunit 2 (GINS2) plays an essential role in the initiation and elongation of DNA replication. Although there have been studies revealing the prognostic significance of GINS2 in breast cancer and lung cancer, its involvement and function in gastric cancer need to be elucidated. We retrospectively enrolled a cohort of gastric adenocarcinoma patients after surgical resection (n = 123). By analyzing the mRNA and protein levels of GINS2 in tissue samples, we found that GINS2 presented a higher expression in tumor tissues than in adjacent normal stomach tissues. Besides, GINS2 level was positively correlated with tumor size and gastric adenocarcinoma tumor stage, implying its potential role as a tumor promoter. Univariate and multivariate analyses identified that patients with lower GINS2 showed a better overall survival compared to those with higher GINS2 expression. In addition, cellular and xenograft experiments confirmed the role of GINS2 in facilitating tumor proliferation both in vitro and in vivo. To our knowledge, this is the initial finding on GINS2 in promoting gastric adenocarcinoma progression. In conclusion, our study revealed a pro-oncogenic role of GINS2 in gastric cancer.