FBLN5 Is an Underlying Common Tumor Suppressor in Breast Cancer and Thyroid Cancer

Abstract Background: Breast cancer (BC) patients have a greater risk of developing thyroid cancer (TC) than the general population. Similarly, TC patients are more likely to develop BC, suggesting an underlying common etiology. In this study, we sought to identify the potential cross-talking pathway and related molecular mechanisms conferring to the sequential development of BC and TC.Methods: We first used Multiple Primary-Standardized Incidence Ratios (MP-SIR) Program of SEER*Stat to calculate SIR to confirm the relationship between BC and TC. Then the RNA-seq was downloaded from The Cancer Genome Atlas (TCGA). And we built a co-expression network via Weighted Gene Co-expression Network Analysis (WGCNA) and obtained the most significant modules. The key genes were obtained by differential gene expression (DGE) analysis and WGCNA analysis. Furthermore, String database and Cytoscape software were used to construct protein-protein interactions (PPI), and defined the maximum Maximal Clique Centrality (MCC) value as hub gene.Then we performed prognosis analysis on the hub genes and obtained the prognostic genes of BC and TC. Finally, gene set enrichment analysis (GSEA) was used to investigate the molecular pathways associated with prognostic gene expressed both in BC and TC.Results: From the SEER database, we found that the risk of developing BC in TC patients was SIR 1.12, 95% CI [1.07, 1.18], and the risk of developing BC in TC patients was SIR 1.29, 95% CI [1.23, 1.26]. Fifty-nine key genes obtained by differential expression analysis and WGCNA identify that PI3K/AKT was the most enriched pathway in BC and TC. In addition, the Recombinant Fibulin 5 (FBLN5) was shown to be of significant prognostic value for both BC and TC and was down-regulated in BC and TC tissues. GSEA demonstrated that FBLN5 enrichment pathways associated with BC and TC mainly included: B cell receptor signaling pathway, steroid hormone biosynthesis, and pathways in cancer.Conclusions: The PI3K/AKT signaling is most co-enriched pathway in BC and TC. FBLN5 is the most relevant prognostic gene and an underlying common tumor suppressor in both BC and TC, with down-stream pathways involving immunity, hormone biosynthesis and carcinogenesis.

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High expression of JAM2 indicates better prognosis and immunotherapy response in breast cancer

10.1101/2020.12.11.421081 ◽

2020 ◽

Author(s):

Yang Peng ◽

Chi Qu ◽

Yingzi Zhang ◽

Beige Zong ◽

Yong Fu ◽

...

Keyword(s):

Breast Cancer ◽

Molecular Mechanisms ◽

Potential Role ◽

Molecular Taxonomy ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Expression ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Chemotherapy Drugs

In our study, multiple databases were used to explore the potential role and underlying mechanism of junctional adhesion molecule B (JAM2) in breast cancer (BRCA). The data of JAM2 were downloaded from The Cancer Cell Line Encyclopedia (CCLE), the Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Receiver operating characteristic (ROC) curve analysis was performed to analyze the area under the curve (AUC) of JAM2 expression correlated with normal breast tissue and breast cancer tissue. Gene set enrichment analysis (GSEA) was used to identify the potential biological mechanisms of the JAM2. The expression of JAM2 mRNA was downregulated in most tumors, including BRCA, which may be due to the hypermethylated status. The AUCs, which were 0.929 and 0.887 by the logistic regression and random forest algorithms, indicated that JAM2 mRNA expression has good diagnostic value in BRCA. Univariate and multivariate analyses indicated JAM2 as an independent prognostic factor for the overall survival of BRCA patients in both the TCGA cohort (HR = 0.62, P = 0.034) and METABRIC cohort (HR = 0.77, P = 0.001). GSEA showed that multiple tumor pathways were suppressed in the JAM2 high expression group. The expression of JAM2 was most positively related to the epithelial-mesenchymal transition (EMT) score (r = 0.38; P <0.01) by the reverse-phase protein array (RPPA) analysis. Patients with high JAM2 expression may be more sensitive to immunotherapy. 18 chemotherapy drugs that patients in the JAM2 low expression group were more sensitive to being identified. Our results demonstrated the diagnostic and prognostic value of JAM2. Analysis of the molecular mechanisms indicates the potential role of JAM2 as a tumor suppressor, and high JAM2 expression may predict a better immunotherapy response in BRCA.

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Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

International Journal of Molecular Sciences ◽

10.3390/ijms22126505 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6505

Author(s):

Jishizhan Chen ◽

Jia Hua ◽

Wenhui Song

Keyword(s):

Stem Cells ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Negative Control ◽

Gene Set Enrichment Analysis ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction ◽

Two Samples ◽

Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

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Comparison of SYK Signaling Networks Reveals the Potential Molecular Determinants of Its Tumor-Promoting and Suppressing Functions

Biomolecules ◽

10.3390/biom11020308 ◽

2021 ◽

Vol 11 (2) ◽

pp. 308

Author(s):

Marion Buffard ◽

Aurélien Naldi ◽

Gilles Freiss ◽

Marcel Deckert ◽

Ovidiu Radulescu ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Suppressor ◽

Signaling Pathways ◽

Burkitt Lymphoma ◽

Molecular Mechanisms ◽

Signal Propagation ◽

Tumor Formation ◽

Tissue Type ◽

Signaling Networks ◽

Proteomic Profiling

Spleen tyrosine kinase (SYK) can behave as an oncogene or a tumor suppressor, depending on the cell and tissue type. As pharmacological SYK inhibitors are currently evaluated in clinical trials, it is important to gain more information on the molecular mechanisms underpinning these opposite roles. To this aim, we reconstructed and compared its signaling networks using phosphoproteomic data from breast cancer and Burkitt lymphoma cell lines where SYK behaves as a tumor suppressor and promoter. Bioinformatic analyses allowed for unveiling the main differences in signaling pathways, network topology and signal propagation from SYK to its potential effectors. In breast cancer cells, the SYK target-enriched signaling pathways included intercellular adhesion and Hippo signaling components that are often linked to tumor suppression. In Burkitt lymphoma cells, the SYK target-enriched signaling pathways included molecules that could play a role in SYK pro-oncogenic function in B-cell lymphomas. Several protein interactions were profoundly rewired in the breast cancer network compared with the Burkitt lymphoma network. These data demonstrate that proteomic profiling combined with mathematical network modeling allows untangling complex pathway interplays and revealing difficult to discern interactions among the SYK pathways that positively and negatively affect tumor formation and progression.

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Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.671639 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhimin Shen ◽

Mingduan Chen ◽

Fei Luo ◽

Hui Xu ◽

Peipei Zhang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Differential Expression Analysis ◽

Biological Significance ◽

The Cancer Genome Atlas ◽

Paclitaxel Resistance ◽

Bioinformatics Analyses ◽

Key Genes

Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.

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Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

BioMed Research International ◽

10.1155/2021/7928051 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Hao Guo ◽

Jing Zhou ◽

Yanjun Zhang ◽

Zhi Wang ◽

Likun Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Model ◽

Molecular Mechanisms ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Prognostic Predictor ◽

Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

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Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.628136 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dongfang Jia ◽

Cheng Chen ◽

Chen Chen ◽

Fangfang Chen ◽

Ningrui Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Gene Expression ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Gene Expression Profiles ◽

Diagnostic Methods ◽

The Cancer Genome Atlas ◽

Support Vector ◽

Hub Genes

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein–protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

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Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.607090 ◽

2021 ◽

Vol 8 ◽

Author(s):

Liang-Hao Zhang ◽

Long-Qing Li ◽

Yong-Hao Zhan ◽

Zhao-Wei Zhu ◽

Xue-Pei Zhang

Keyword(s):

Bladder Cancer ◽

Stromal Cells ◽

Molecular Mechanisms ◽

Proportional Hazards ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Validation Set

BackgroundIdentify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.Materials and MethodsOne RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.ResultsThis signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.ConclusionThe novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

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Loss of G-Protein Pathway Suppressor 2 Promotes Tumor Growth Through Activation of AKT Signaling

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.608044 ◽

2021 ◽

Vol 8 ◽

Author(s):

Stefanie Chan ◽

Emma Smith ◽

Yuan Gao ◽

Julian Kwan ◽

Benjamin C. Blum ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Suppressor ◽

Tumor Growth ◽

G Protein ◽

Molecular Mechanisms ◽

Akt Signaling ◽

Proteomic Profiling ◽

Potential Biomarker ◽

Regulated Gene Expression

G Protein Suppressor 2 (GPS2) is a multifunctional protein that exerts important roles in inflammation and metabolism in adipose, liver, and immune cells. GPS2 has recently been identified as a significantly mutated gene in breast cancer and other malignancies and proposed to work as a putative tumor suppressor. However, molecular mechanisms by which GPS2 prevents cancer development and/or progression are largely unknown. Here, we have profiled the phenotypic changes induced by GPS2 depletion in MDA-MB-231 triple negative breast cancer cells and investigated the underlying molecular mechanisms. We found that GPS2-deleted MDA-MB-231 cells exhibited increased proliferative, migratory, and invasive properties in vitro, and conferred greater tumor burden in vivo in an orthotopic xenograft mouse model. Transcriptomic, proteomic and phospho-proteomic profiling of GPS2-deleted MBA-MB-231 revealed a network of altered signals that relate to cell growth and PI3K/AKT signaling. Overlay of GPS2-regulated gene expression with MDA-MB-231 cells modified to express constitutively active AKT showed significant overlap, suggesting that sustained AKT activation is associated with loss of GPS2. Accordingly, we demonstrate that the pro-oncogenic phenotypes associated with GPS2 deletion are rescued by pharmacological inhibition of AKT with MK2206. Collectively, these observations confirm a tumor suppressor role for GPS2 and reveal that loss of GPS2 promotes breast cancer cell proliferation and tumor growth through uncontrolled activation of AKT signaling. Moreover, our study points to GPS2 as a potential biomarker for a subclass of breast cancers that would be responsive to PI3K-class inhibitor drugs.

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