Aggresome–Autophagy Associated Gene HDAC6 Is a Potential Biomarker in Pan-Cancer, Especially in Colon Adenocarcinoma

BackgroundHistone deacetylase 6 (HDAC6) regulates cytoplasmic signaling networks through the deacetylation of various cytoplasmic substrates. Recent studies have identified the role of HDAC6 in tumor development and immune metabolism, but its specific function remains unclear.MethodsThe current study determined the role of HDAC6 in tumor metabolism and tumor immunity through a multi-database pan-cancer analysis. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) datasets were used to determine the expression levels, prognosis, tumor progression, immune checkpoints, and immune metabolism of HDAC6 in 33 tumors. Pathways, immune checkpoints, immune neoantigens, immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), DNA mismatch repair (MMR), and the value of methyltransferases. The R package was used for quantitative analysis and panoramic description.ResultsIn the present study, we determined that HDAC6 is differentially expressed in pan carcinomas, and by survival, we found that HDAC6 was generally associated with the prognosis of pancreatic adenocarcinoma, Thymoma, and uveal melanoma, where low expression of HDAC6 had a significantly worse prognosis. Secondly, through this experiment, we confirmed that HDAC6 expression level was associated with tumor immune infiltration and tumor microenvironment, especially in PAAD. Finally, HDAC6 was associated with immune neoantigen and immune checkpoint gene expression profiles in all cancers in addition to TMB and MSI in pan-cancers.ConclusionHDAC6 is differentially expressed in pan-cancers and plays an essential role in tumor metabolism and immunity. HDAC6 holds promise as a tumor potential prognostic marker, especially in colon cancer.

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Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽

10.3390/metabo11030180 ◽

2021 ◽

Vol 11 (3) ◽

pp. 180

Author(s):

Christina Mertens ◽

Matthias Schnetz ◽

Claudia Rehwald ◽

Stephan Grein ◽

Eiman Elwakeel ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Progression ◽

Tumor Cells ◽

Cancer Progression ◽

Lung Metastases ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Lipocalin 2 ◽

Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

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The role of ferroptosis in breast cancer patients: a comprehensive analysis

Cell Death Discovery ◽

10.1038/s41420-021-00473-5 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Zeng-Hong Wu ◽

Yun Tang ◽

Hong Yu ◽

Hua-Dong Li

Keyword(s):

Breast Cancer ◽

T Cell ◽

Signaling Pathway ◽

Differentially Expressed Genes ◽

Function Analysis ◽

Cancer Prognosis ◽

Differentially Expressed ◽

Immune Checkpoints ◽

Cell Functions

AbstractBreast cancer (BC) affects the breast tissue and is the second most common cause of mortalities among women. Ferroptosis is an iron-dependent cell death mode that is characterized by intracellular accumulation of reactive oxygen species (ROS). We constructed a prognostic multigene signature based on ferroptosis-associated differentially expressed genes (DEGs). Moreover, we comprehensively analyzed the role of ferroptosis-associated miRNAs, lncRNAs, and immune responses. A total of 259 ferroptosis-related genes were extracted. KEGG function analysis of these genes revealed that they were mainly enriched in the HIF-1 signaling pathway, NOD-like receptor signaling pathway, central carbon metabolism in cancer, and PPAR signaling pathway. Fifteen differentially expressed genes (ALOX15, ALOX15B, ANO6, BRD4, CISD1, DRD5, FLT3, G6PD, IFNG, NGB, NOS2, PROM2, SLC1A4, SLC38A1, and TP63) were selected as independent prognostic factors for BC patients. Moreover, T cell functions, including the CCR score, immune checkpoint, cytolytic activity, HLA, inflammation promotion, para-inflammation, T cell co-stimulation, T cell co-inhibition, and type II INF responses were significantly different between the low-risk and high-risk groups of the TCGA cohort. Immune checkpoints between the two groups revealed that the expressions of PDCD-1 (PD-1), CTLA4, LAG3, TNFSF4/14, TNFRSF4/8/9/14/18/25, and IDO1/2 among others were significantly different. A total of 1185 ferroptosis-related lncRNAs and 219 ferroptosis-related miRNAs were also included in this study. From the online database, we identified novel ferroptosis-related biomarkers for breast cancer prognosis. The findings of this study provide new insights into the development of new reliable and accurate cancer treatment options.

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Molecular and Clinical Characterization of LAG3 in Breast Cancer Through 2994 Samples

10.21203/rs.3.rs-36422/v1 ◽

2020 ◽

Author(s):

Qiang Liu ◽

Yihang Qi ◽

Jie Zhai ◽

Xiangyi Kong ◽

Xiangyu Wang ◽

...

Keyword(s):

Breast Cancer ◽

Immune Response ◽

Cancer Immunotherapy ◽

Cancer Patients ◽

Immune Cell ◽

Immune Checkpoints ◽

Cell Populations ◽

Potential Biomarker ◽

The Impact

Abstract Background Despite the promising impact of cancer immunotherapy targeting CTLA4 and PD1/PDL1, a large number of cancer patients fail to respond. LAG3 (Lymphocyte Activating 3), also named CD233, is a protein Coding gene served as alternative inhibitory receptors to be targeted in the clinic. The impact of LAG3 on immune cell populations and co-regulation of immune response in breast cancer remained largely unknown. Methods To characterize the role of LAG3 in breast cancer, we investigated transcriptome data and associated clinical information derived from a total of 2994 breast cancer patients. Results We observed that LAG3 was closely correlated with major molecular and clinical characteristics, and was more likely to be enriched in higher malignant subtype, suggesting LAG3 was a potential biomarker of triple-negative breast cancer. Furthermore, we estimated the landscape of relationship between LAG3 and ten types of cell populations in breast cancer. Gene ontology analysis revealed LAG3 were strongly correlated with immune response and inflammatory activities. We investigated the correlation pattern between LAG3 and immune modulators in pan-cancer, especially the synergistic role of LAG3 with other immune checkpoints members in breast cancer. Conclusions LAG3 expression was closely related to malignancy of breast cancer and might serve as a potential biomarker; LAG3 might plays an important role in regulating tumor immune microenvironment, not only T cells, but also other immune cells. More importantly, LAG3 might synergize with CTLA4, PD1/ PDL1 and other immune checkpoints, thereby lending more evidences to combination cancer immunotherapy by targeting LAG3, PD1/PDL1, and CTLA4 together.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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MicroRNA and circRNA Expression Analysis in a Zbtb1 Gene Knockout Monoclonal EL4 Cell Line

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.706919 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jun-Hong Wang ◽

Chun-Wei Shi ◽

Yi-Yuan Lu ◽

Yan Zeng ◽

Ming-Yang Cheng ◽

...

Keyword(s):

T Cell ◽

Cell Line ◽

Gene Deletion ◽

Cell Lymphoma ◽

Gene Knockout ◽

Expression Profiles ◽

T Cell Lymphoma ◽

Differentially Expressed ◽

Btb Domain

Zinc finger and BTB domain containing 1(Zbtb1) is a transcriptional suppressor protein, and a member of the mammalian Zbtb gene family. Previous studies have shown that Zbtb1 is essential for T-cell development. However, the role of Zbtb1 in T-cell lymphoma is undetermined. In this study, an EL4 cell line with Zbtb1 deletion was constructed using the CRISPR-Cas9 technique. The expression profiles of microRNA and circRNA produced by the control and gene deletion groups were determined by RNA-seq. In general, 24 differentially expressed microRNA and 16 differentially expressed circRNA were found between normal group and gene deletion group. Through further analysis of differentially expressed genes, GO term histogram and KEGG scatter plot were drawn, and three pairs of miRNA and circRNA regulatory relationships were found. This study describes the differentially expressed microRNA and circRNA in normal and Zbtb1-deficient EL4 cell lines, thus providing potential targets for drug development and clinical treatment of T-cell lymphoma.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

10.21203/rs.3.rs-19035/v1 ◽

2020 ◽

Author(s):

Yanjie Han ◽

Xinxin Li ◽

Jiliang Yan ◽

Chunyan Ma ◽

Xin Wang ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Growth Factor Receptor ◽

Principal Component ◽

Distant Metastases ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

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Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

International Journal of Molecular Sciences ◽

10.3390/ijms21176087 ◽

2020 ◽

Vol 21 (17) ◽

pp. 6087

Author(s):

Yunzhen Wei ◽

Limeng Zhou ◽

Yingzhang Huang ◽

Dianjing Guo

Keyword(s):

Cancer Biology ◽

Noncoding Rna ◽

The Cancer Genome Atlas ◽

Dynamic Changes ◽

Computational Framework ◽

Potential Biomarker ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tcga Dataset ◽

Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.595361 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xin Qiu ◽

Qin-Han Hou ◽

Qiu-Yue Shi ◽

Hai-Xing Jiang ◽

Shan-Yu Qin

Keyword(s):

Oxidative Stress ◽

Gene Expression ◽

Pancreatic Cancer ◽

Cancer Progression ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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