scholarly journals Development and Validation of an Autophagy-Related Signature for Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chang Liu ◽  
Wenling Wu ◽  
Meng Xu ◽  
Jinglin Mi ◽  
Longjiang Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cell Lines ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Clinicopathological Parameters ◽  
Tissue Samples ◽  
Prognosis Prediction ◽  
Potential Biomarker ◽  
Prognosis Prediction Model

Introduction. HNSCC is the sixth most frequent type of malignant carcinoma with a low prognosis rate. In addition, autophagy is important in cancer development and progression. The purpose of this study is to investigate the potential significance of ARGs in the diagnosis and treatment of HNSCC. Materials and Methods. Expression data and clinical information of HNSCC samples were collected from the TCGA database, and a list of ARGs was obtained from the MSigDB. Then, we used R software to perform differential expression analysis and functional enrichment analysis. Further analysis was also performed to find out the survival-related ARGs in HNSCC, and two prognosis-related ARGs, FADD and NKX2-3, were selected to construct a prognosis prediction model. Moreover, some methods were applied to validate the prognosis prediction model. Finally, we used cell lines and clinical tissue samples of HNSCC to analyze the importance of FADD and NKX2-3. Results. We screened a total of 38 differentially expressed ARGs, and enrichment analysis showed that these genes were mainly involved in autophagy. Then, we selected FADD and NKX2-3 to construct a prognosis model and the risk score calculated by the model was proved to be effective in predicting the survival of HNSCC patients. Additionally, significant differences of the clinicopathological parameters could also be observed in the risk scores and the expression of NKX2-3 and FADD. The expression of FADD and NKX2-3 in cell lines and HNSCC tissue samples also showed the same trends. Conclusions. ARGs may be a potential biomarker for HNSCC prognosis, and targeted therapies for FADD and NKX2-3 are possible to be a new strategy of HNSCC treatment.

scholarly journals miR-154 Influences HNSCC Development and Progression through Regulation of the Epithelial-To-Mesenchymal Transition Process and Could Be Used as a Potential Biomarker

Biomedicines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1894
Author(s):  
Weronika Tomaszewska ◽  
Joanna Kozłowska-Masłoń ◽  
Dawid Baranowski ◽  
Anna Perkowska ◽  
Sandra Szałkowska ◽  
...  
Keyword(s):  
Immune Response ◽  
Epithelial To Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinicopathological Parameters ◽  
Mesenchymal Transition ◽  
Diagnostic Potential ◽  
Potential Biomarker

MicroRNAs and their role in cancer have been extensively studied for the past decade. Here, we analyzed the biological role and diagnostic potential of miR-154-5p and miR-154-3p in head and neck squamous cell carcinoma (HNSCC). miRNA expression analyses were performed using The Cancer Genome Atlas (TCGA) data accessed from cBioPortal, UALCAN, Santa Cruz University, and Gene Expression Omnibus (GEO). The expression data were correlated with clinicopathological parameters. The functional enrichment was assessed with Gene Set Enrichment Analysis (GSEA). The immunological profiles were assessed using the ESTIMATE tool and RNAseq data from TCGA. All statistical analyses were performed with GraphPad Prism and Statistica. The study showed that both miR-154-5p and miR-154-3p were downregulated in the HNSCC samples and their expression levels correlated with tumor localization, overall survival, cancer stage, tumor grade, and HPV p16 status. GSEA indicated that individuals with the increased levels of miR-154 had upregulated AKT-MTOR, CYCLIN D1, KRAS, EIF4E, RB, ATM, and EMT gene sets. Finally, the elevated miR-154 expression correlated with better immune response. This study showed that miR-154 is highly involved in HNSCC pathogenesis, invasion, and immune response. The implementation of miR-154 as a biomarker may improve the effectiveness of HNSCC treatment.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

scholarly journals MicroRNA-550a is associated with muscle system conferring poorer survival for esophageal cancer

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Housong Hong ◽  
Taisheng Liu ◽  
Huazhen Wu ◽  
Jinye Zhang ◽  
Xiaoshun Shi ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Survival Time ◽  
Therapeutic Target ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Muscle System ◽  
Expression Levels ◽  
Potential Biomarker

Abstract Background Esophageal cancer (ESCA) is one of the most common cancers in the digestive tract. Approximately 300000 people on an average die of ESCA per year worldwide. The determination of key microRNAs for the prognosis of ESCA is of indispensable significance in the clinical treatment. Methods The differentially expressed microRNAs were screened by analyzing The Cancer Genome Atlas (TCGA) database. By using the survival data of the database, we analyzed correlation between patients’ survival time and miR-550a expression levels. Differential expression analysis and gene set enrichment analysis were performed using the targeted data. Results It was found that patients with high miR-550a expression levels had shorter survival time. Data mining and signal pathway enrichment analysis of TCGA database showed that abnormal miR-550a expressions affected the recurrence of tumors by the muscle system regulation. Conclusions Through the proposed investigation, miR-550a is found to be a potential biomarker as well as non-coding therapeutic target for esophagus cancer. These results suggest that miR-550a may serve as a therapeutic target and predictor for ESCA survival.

scholarly journals Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lingling Gao ◽  
Xiao Li ◽  
Qian Guo ◽  
Xin Nie ◽  
Yingying Hao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Signaling Pathway ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Cell Junction ◽  
Immune Infiltration ◽  
Potential Biomarker

Abstract Background Plakophilins (PKPs) are widely involved in gene transcription, translation, and signal transduction, playing a crucial role in tumorigenesis and progression. However, the function and potential mechanism of PKP1/2/3 in ovarian cancer (OC) remains unclear. It’s of great value to explore the expression and prognostic values of PKP1/2/3 and their potential mechanisms, immune infiltration in OC. Methods The expression levels, prognostic values and genetic variations of PKP1/2/3 in OC were explored by various bioinformatics tools and databases, and PKP2/3 were selected for further analyzing their regulation network and immune infiltration. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment were also conducted. Finally, the expression and prognosis of PKP2 were validated by immunohistochemistry. Results The expression level and prognosis of PKP1 showed little significance in ovarian cancer, and the expression of PKP2/3 mRNA and protein were upregulated in OC, showing significant correlations with poor prognosis of OC. Functional enrichment analysis showed that PKP2/3 and their correlated genes were significantly enriched in adaptive immune response, cytokine receptor activity, organization of cell–cell junction and extracellular matrix; KEGG analysis showed that PKP2/3 and their significantly correlated genes were involved in signaling pathways including cytokine-mediated signaling pathway, receptor signaling pathway and pathways in cancer. Moreover, PKP2/3 were correlated with lymphocytes and immunomodulators. We confirmed that high expression of PKP2 was significantly associated with advanced stage, poor differentiation and poor prognosis of OC patients. Conclusion Members of plakophilins family showed various degrees of abnormal expressions and prognostic values in ovarian cancer. PKP2/3 played crucial roles in tumorigenesis, aggressiveness, malignant biological behavior and immune infiltration of OC, and can be regarded as potential biomarker for early diagnosis and prognosis evaluation in OC.

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Vol 49 (D1) ◽  
pp. D1420-D1430
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Jun Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

scholarly journals Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Sayuri Higaki ◽  
Taiki Sugimoto ◽  
Takashi Sakurai ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
High Risk ◽  
Prediction Model ◽  
Target Genes ◽  
Meta Analysis ◽  
Hub Genes ◽  
Blood Samples ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model

Abstract Background Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required. Methods We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]). Results The final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049). Conclusions Our proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

scholarly journals Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 903 ◽  
Author(s):  
Antonio Federico ◽  
Angela Serra ◽  
My Kieu Ha ◽  
Pekka Kohonen ◽  
Jang-Sik Choi ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Data Preprocessing ◽  
Functional Enrichment Analysis ◽  
Toxicity Assessment ◽  
Functional Enrichment ◽  
Quality Data ◽  
Chemical Toxicity ◽  
Transcriptomics Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

scholarly journals Genomic profiling of cancerous patients identifies FPR2 as an alternative immunotherapeutic target in glioblastoma multiforme

2020 ◽  
Author(s):  
Yuqing Yang ◽  
Ting Sun ◽  
Chuchen Qiu ◽  
Dongjing Chen ◽  
You Wu
Keyword(s):  
Glioblastoma Multiforme ◽  
Tumor Progression ◽  
Differentially Expressed Genes ◽  
Primary Tumor ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Control Group ◽  
Western Blots ◽  
Potential Biomarker

ABSTRACTBackgroundGlioblastoma multiforme (GBM) is a type of high-grade brain tumor known for its proliferative, invasive property, and low survival rate. Recently, with the advancement in therapeutics for tumors such as targeted therapy, individual cancer-specific biomarkers could be recognized as targets for curative purposes. This study identified six differentially expressed genes that have shown significant implications in clinical field, including FPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3. FPR2 was of the same protein family with FPR1, and the latter has been repeatedly reported to promote motility and invasiveness of multiple tumor forms.MethodsThe gene expression profiling of 40 GBM samples and five normal samples from the TCGA database were comprehensively analyzed. The differentially expressed genes (DEGs) were identified using R package and screened by enrichment analysis and examination of protein–protein interaction networks, in order to further explore the functions of DEGs with the highest association with clinical traits and to find hub genes. A qRT-PCR and Western blots were conducted to verify the results of this study.ResultsOur investigation showed that FPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3 were significantly up-regulated in GBM primary tumor compared to the control group. Functional enrichment analysis of the DEGs demonstrated that biological functions related to immune systems, cell division and cell cycle were significantly increased, which were closely related to tumor progression and development. Downstream construction of PPI network analysis indicated that FPR2 was a hub gene involved in high level of interaction with CR3 and VEGFA, which played a key role in inflammatory pathways and cellular dysfunction.ConclusionFPR2, VEGFA, SERPINA1, SOX2, PBK, and ITGB3 were significantly over-expressed in primary tumor samples of GBM patients and were involved in cellular functions and pathways contributing to tumor progression. Out of these six pivotal genes, we intensively focused on FPR2, and our analysis and experimental data both suggested its efficacy as a potential biomarker, serving as an alternative immunotherapeutic target for glioblastoma multiforme.

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