Upregulated NLGN1 predicts poor survival in colorectal cancer

Abstract Background Neuroligin1 (NLGN1) is a main component of excitatory glutamatergic synapses complex and is important for synapse assembly and function. The clinical value of NLGN1 in colorectal cancer (CRC) is not clear. Methods We obtained the expression data of 1143 CRC patients from 3 independent Gene Expression Omnibus (GEO) datasets (GSE32323, GSE24551, GSE39582) and The Cancer Genome Atlas (TCGA) to make the comparison of the NLGN1 expression level between CRC tissues and matched noncancerous tissues, and to evaluate its value in predicting survival of CRC patients. At the protein level, these results were further confirmed by immunohistochemical staining of 52 CRC samples in our own centre. Finally, the function of NLGN1 was explored by gene set enrichment analysis (GSEA). Results Increased mRNA and protein levels of NLGN1 expression were associated with worse overall survival or recurrence-free survival in CRC patients from 2 GEO datasets, the TCGA database, and our cohort. In addition, multivariate regression analysis showed that NLGN1 was an independent poor prognostic factor of survival in patients with CRC in TCGA database (OR = 2.524, P = 0.010). Functional analysis revealed that NLGN1 was correlated with function involving the Hedgehog signaling pathway, mismatch repair process, and some material metabolism processes. Conclusions This study is the first to implicate and verify NLGN1 as a new poor prognostic marker for CRC.

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Upregulated NLGN1 predicts poor survival in colorectal cancer

10.21203/rs.3.rs-275861/v1 ◽

2021 ◽

Author(s):

Qian Yu ◽

Xiaojie Wang ◽

Yinghong Yang ◽

Pan Chi ◽

Jianping Huang ◽

...

Keyword(s):

Colorectal Cancer ◽

Hedgehog Signaling ◽

Enrichment Analysis ◽

Multivariate Regression Analysis ◽

Repair Process ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Poor Prognostic Factor

Abstract Background: Neuroligin1 (NLGN1) is a main component of excitatory glutamatergic synapses complex and is important for synapse assembly and function. The clinical value of NLGN1 in colorectal cancer (CRC) is not clear. Methods: We obtained the expression data of 1143 CRC patients from 3 independent Gene Expression Omnibus (GEO) datasets (GSE32323, GSE24551, GSE39582) and The Cancer Genome Atlas (TCGA) to make the comparison of the NLGN1 expression level between CRC tissues and matched noncancerous tissues, and to evaluate its value in predicting survival of CRC patients. At the protein level, these results were further confirmed by immunohistochemical staining of 52 CRC samples in our own centre. Finally, the function of NLGN1 was explored by gene set enrichment analysis (GSEA). Results: Increased mRNA and protein levels of NLGN1 expression were associated with worse overall survival or recurrence-free survival in CRC patients from 2 GEO datasets, the TCGA database, and our cohort. In addition, multivariate regression analysis showed that NLGN1 was an independent poor prognostic factor of survival in patients with CRC in TCGA database (OR = 2.524, P = 0.010). Functional analysis revealed that NLGN1 was correlated with function involving the Hedgehog signaling pathway, mismatch repair process, and some material metabolism processes. Conclusions: This study is the first to implicate and verify NLGN1 as a new poor prognostic marker for CRC.

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Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.573295 ◽

2020 ◽

Vol 10 ◽

Author(s):

Fang-Ze Wei ◽

Shi-Wen Mei ◽

Zhi-Jie Wang ◽

Jia-Nan Chen ◽

Hai-Yu Shen ◽

...

Keyword(s):

Colorectal Cancer ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Ncbi Gene Expression Omnibus ◽

Public Datasets

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

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Mind Bomb 1 Promotes Pancreatic Cancer Proliferation by Activating β-Catenin Signaling

Journal of Nanoscience and Nanotechnology ◽

10.1166/jnn.2020.18755 ◽

2020 ◽

Vol 20 (12) ◽

pp. 7276-7282

Author(s):

Xiao Fu ◽

Neng Tang ◽

Weiqi Xie ◽

Liang Mao ◽

Yudong Qiu

Keyword(s):

Pancreatic Cancer ◽

Cancer Metastasis ◽

Mrna Level ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pancreatic Tumors ◽

Mrna Levels ◽

Cancer Proliferation

Mind bomb 1 (MIB1), an E3 ligase, plays a vital role in chemo-resistance and cancer metastasis. According to The Cancer Genome Atlas (TCGA), MIB1 gene is preferentially amplified in pancreatic cancer. Copy number alterations in MIB1 gene are associated with worse survival. Gene Expression Omnibus (GEO) also showed that pancreatic cancer with high mRNA level of MIB1 tend to be more resistant to gemcitabine and higher mRNA levels of MIB1 are found in pancreatic tumors compared with adjacent normal tissues. MIB1 knockdown (KD) in Panc-1 and HPAF2 cell lines significantly inhibit proliferation and colony formation of pancreatic cancer. The gene set enrichment analysis (GSEA) has also showed that β-catenin is the downstream of MIB1. Western blot analysis showed that total and active β-catenin levels are decreased in MIB1 KD cells. β-catenin inhibitor also inhibits proliferation of Panc-1 and HPAF2 cells. We in this study implanted HPAF2 scramble and MIB1 KD cells orthotopically in athymic nude mice. Gemcitabine was used to treat the mice. Results revealed that after MIB1 KD HPAF2 cells were more sensitive to gemcitabine. In conclusion, we demonstrated that MIB1 promotes pancreatic cancer proliferation through activating β-catenin signaling. MIB1 may thus be a therapeutic target in pancreatic cancer therapy.

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Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽

10.1186/s12885-021-07995-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jingyi Chen ◽

Yuxuan Song ◽

Mei Li ◽

Yu Zhang ◽

Tingru Lin ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Target Genes ◽

Immune Cell ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

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Prognostic significance and oncogene function of cathepsin A in hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-021-93998-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Huaxiang Wang ◽

Fengfeng Xu ◽

Fang Yang ◽

Lizhi Lv ◽

Yi Jiang

Keyword(s):

Hepatocellular Carcinoma ◽

Oxidative Phosphorylation ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Normal Liver ◽

Diagnostic Value ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Lysosomal Protease

AbstractCathepsin A (CTSA) is a lysosomal protease that regulates galactoside metabolism. The previous study has shown CTSA is abnormally expressed in various types of cancer. However, rarely the previous study has addressed the role of CTSA in hepatocellular carcinoma (HCC) and its prognostic value. To study the clinical value and potential function of CTSA in HCC, datasets from the Cancer Genome Atlas (TCGA) database and a 136 HCC patient cohort were analyzed. CTSA expression was found to be significantly higher in HCC patients compared with normal liver tissues, which was supported by immunohistochemistry (IHC) validation. Both gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that CTSA co-expressed genes were involved in ATP hydrolysis coupled proton transport, carbohydrate metabolic process, lysosome organization, oxidative phosphorylation, other glycan degradation, etc. Survival analysis showed a significant reduction both in overall survival (OS) and recurrence-free survival (RFS) of patients with high CTSA expression from both the TCGA HCC cohort and 136 patients with the HCC cohort. Furthermore, CTSA overexpression has diagnostic value in distinguishing between HCC and normal liver tissue [Area under curve (AUC) = 0.864]. Moreover, Gene set enrichment analysis (GSEA) showed that CTSA expression correlated with the oxidative phosphorylation, proteasome, and lysosome, etc. in HCC tissues. These findings demonstrate that CTSA may as a potential diagnostic and prognostic biomarker in HCC.

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A Mutational Signature that Predicts Prognosis and Benefit of Immune Checkpoint Blockade in Colorectal Cancer

10.21203/rs.3.rs-122186/v1 ◽

2020 ◽

Author(s):

Liang Xu ◽

Yanyun Lin ◽

Xijie Chen ◽

Lisheng Zheng ◽

Yufeng Cheng ◽

...

Keyword(s):

Colorectal Cancer ◽

Immune Cell ◽

Enrichment Analysis ◽

Immune Checkpoint Blockade ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Center ◽

Prognostic Information ◽

Mutational Signature

Abstract Background: Colorectal cancer (CRC) is characterized by broad genomic and transcriptional heterogeneity. However, the genomic basis of this variability remains poorly understood. Our pilot study identified mutated genes were associated with immune infiltration. This study aims to explore a novel mutational signature (MS) in tumor microenvironment (TME) of CRC.Methods: We integrated single nucleotide variation and transcriptome data and collected corresponding clinicopathologic information from 1,133 and 588 CRC patients of Memorial Sloan Kettering Cancer Center and The Cancer Genome Atlas databases, respectively. Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of CRC based on the immune genomes of 29 immune signatures. CIBERSORT was used to analyze the infiltration of 22 immune cell types in the TME and immune-related gene expression CRC tissues. Results: In the training cohort, we identified a novel MS consisting of 27 genes and generated a prognostic model that classifies patients into high- and low-risk groups. The low-risk group was associated with better survival and more tumor mutational burden, microsatellite instability, and mismatch repair deficiency. The data were all verified in the validation set. Further analysis revealed that the MS was associated with tumor immunogenicity and immunocyte infiltration, and the determined risk score (RS) could be an index for the immunity level.Conclusion: We identified a MS that could assist clinicians to select immunotherapy responsive patients and the combination of RS and TNM stage could provide comprehensive prognostic information for CRC.

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Identification of MEG8-miR378d-SOBP Axis as a Novel Regulatory Network and Associated With Immune Infiltrates in Ovarian Carcinoma by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-76980/v1 ◽

2020 ◽

Author(s):

Jian Lei ◽

Zhen-Yu He ◽

Jun Wang ◽

Min Hu ◽

Ping Zhou ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Human Protein Atlas

Abstract BackgroundTo investigate the potential molecular mechanism of ovarian cancer (OC) evolution and immunological correlation using the integrated bioinformatics analysis.MethodsData from the Gene Expression Omnibus (GEO) was used to gain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were completed by utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID). After multiple validation via The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis 2 (GEPIA 2), the Human Protein Atlas (HPA) and Kaplan-Meier (KM) plotter, immune logical relationships of the key gene SOBP were evaluated based on Tumor Immune Estimation Resource (TIMER), and Gene Set Enrichment Analysis (GSEA) software. Finally, the lncRNAs-miRNAs-mRNAs sub-network was predicted by starBase, Targetscan, miRBD, and LncBase, individually. Correlation of expression and prognosis for mRNAs, miRNAs and lncRNAs were confirmed by TCGA, GEPIA 2, starBase, and KM.ResultsA total of 192 shared DEGs were discovered from the four data sets, including 125 upregulated and 67 downregulated genes. Functional enrichment analysis presented that they were mainly enriched in cartilage development, pathway in PI3K-Akt signaling pathway. Lower expression of SOBP was the independent prognostic factor for inferior prognosis in OC patients. Intriguingly, downregulated SOBP enhanced the infiltration levels of B cells, CD8+ T cells, Macrophage, Neutrophil and Dendritic cells. GSEA also disclosed low SOBP showed significantly association with the activation of various immune-related pathways. Finally, we firstly reported that MEG8-miR378d-SOBP axis was linked to development and prognosis of ovarian cancer through regulating cytokines pathway.Conclusions Our study establishes a novel MEG8-miR378d-SOBP axis in the development and prognosis of OC, and the triple sub-network probably affects the progression of ovarian tumor by regulating cytokines pathway.

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Malignant tumor purity reveals the correlation between CD3E and low grade glioma microenvironment

10.21203/rs.3.rs-93487/v1 ◽

2020 ◽

Author(s):

WangRui Liu ◽

Chuanyu Li ◽

Wenhao Xu ◽

Hao Lian ◽

Yuanyuan Qu ◽

...

Keyword(s):

Cox Regression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Low Grade Glioma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Tumor Purity ◽

Immune Related Genes

Abstract Background: Tumor microenvironment (TME) contributes to the initiation and progression of low grade glioma (LGG); however, we are still unclear about the specifics of LGG's TME. Methods: In this article, we selected 161 LGG patients from the Cancer Genome Atlas (TCGA) as data, and calculated the percentage of tumor infiltrating immune cells (TICs) in LGG and the tumor purity of LGG through ESTIMATE and CIBERSORT calculation methods. Immune-related genes were screened out through Cox regression and protein-protein interaction (PPI) network. The data in Gene Expression Omnibus (GEO) was selected to screen out clinically relevant genes. After combining the two, CD3E is selected as the predictor. Finally, we conducted verification at the Affiliated Hospital of YouJiang Medical University for Nationalities (AHYMUN) center. Results: We found that the higher the expression of CD3E, the lower the purity of LGG tumors and the worse the prognosis of patients. Gene Set Enrichment Analysis (GSEA) showed that genes in the high-expressing CD3E group are mainly involved in immune-related activities. This suggests that CD3E may be responsible for regulating LGG's TME and tumor purity.Conclusion: In short, the tumor purity of LGG has a considerable impact on clinical, genomic and biological status. The expression level of CD3E may help doctors evaluate the prognosis of LGG patients and develop personalized immunotherapy plans for patients. Evaluating the ratio of different tumor purity and the new role of CD3E may provide additional insights into the complex role of the LGG microenvironment and clinical treatment.

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Prognostic implications of metabolism-associated gene signatures in colorectal cancer

PeerJ ◽

10.7717/peerj.9847 ◽

2020 ◽

Vol 8 ◽

pp. e9847

Author(s):

Yandong Miao ◽

Qiutian Li ◽

Jiangtao Wang ◽

Wuxia Quan ◽

Chen Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Clinicopathological Feature ◽

Metabolic Genes ◽

Cox Analysis

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

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Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/3036741 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Dong Qi ◽

Kui Chen

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Hedgehog Signaling ◽

Epithelial Mesenchymal Transition ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Major Depressive ◽

Mesenchymal Transition

Aiming at a more comprehensive understanding of the molecular biomarkers and potential mechanisms of major depressive disorder (MDD), from the Gene Expression Omnibus (GEO) database, we first obtained mRNA expression profiles and identified 585 differentially expressed genes (DEGs) through the R software, including 263 upregulated genes and 322 downregulated genes. Then, through the Kyoto Encyclopedia of Genome and Genome (KEGG) pathway and biological process (BP) analysis, we found that the upregulated and downregulated DEGs were abundant in different pathways, respectively. It was noteworthy that upregulated DEGs were the most significantly enriched in the mTOR signaling pathway. Subsequently, through the protein-protein interaction (PPI) network, we identified seven hub genes, namely, EXOSC2, CAMK2A, PRIM1, SMC4, TYMS, CDK6, and RPA2. Finally, through gene set enrichment analysis (GSEA), we obtained that hypoxia, epithelial-mesenchymal transition, hedgehog signaling, and reactive oxygen species pathway were the enriched pathways for MDD patients. The above data results would provide a new direction for the treatment of MDD patients.

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