Clinical challenges of tissue preparation for spatial transcriptome.

Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.

Standardizing workflows in imaging transcriptomics with the abagen toolbox.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

Plant PhysioSpace: a robust tool to compare stress response across plant species.

Generalization of transcriptomics results can be achieved by comparison across experiments. This generalization is based on integration of interrelated transcriptomics studies into a compendium. Such a focus on the bigger picture enables both characterizations of the fate of an organism and distinction between generic and specific responses. Numerous methods for analyzing transcriptomics datasets exist. Yet, most of these methods focus on gene-wise dimension reduction to obtain marker genes and gene sets for, for example, pathway analysis. Relying only on isolated biological modules might result in missing important confounders and relevant contexts. We developed a method called Plant PhysioSpace, which enables researchers to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene sets. Plant PhysioSpace extracts physiologically relevant signatures from a reference dataset (i.e. a collection of public datasets) by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores between the acquired data and the extracted compendium. Because of its robustness against platform bias and noise, Plant PhysioSpace can function as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms, including single-cell technologies. We have also implemented two R packages, one software and one data package, and a Shiny web application to facilitate access to our method and precomputed models.

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes.

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Construction of a 5 immune-related lncRNA-based prognostic model of NSCLC via bioinformatics.

ABSTRACT: Participate in tumorigenic, oncogenic, and tumor suppressive pathways through gene expression regulation. We aimed to build an immune-related long noncoding RNA (lncRNA) prognostic model to enhance nonsmall cell lung cancer (NSCLC) prognostic prediction.The original data were collected from the cancer genome atlas database. Perl and R software were used for statistical analysis. The effects of lncRNAs expression on prognosis were analyzed by Gene Expression Profiling Interactive Analysis. Silico functional analysis were performed by DAVID Bioinformatics Resources.The median risk score as a dividing value separated patients into high- and low-risk groups. These 2 groups had different 5-year survival rates, median survival times, and immune statuses. The 5-lncRNA signature was validated as an independent prognostic factor with high accuracy (area under the receiver operating characteristic = 0.722). Silico functional analysis connected the lncRNAs with immune-related biological processes and pathways in carcinogenesis.The novel immune-related lncRNA prognostic model had significant clinical implication for enhancing lung adenocarcinoma outcome prediction and guiding the choice of treatment.

Identifying key genes and small molecule compounds for nasopharyngeal carcinoma by various bioinformatic analysis.

ABSTRACT: Nasopharyngeal carcinoma (NPC) is one of the most prevalent head and neck cancer in southeast Asia. It is necessary to proceed further studies on the mechanism of occurrence and development of NPC.In this study, we employed the microarray dataset GSE12452 and GSE53819 including 28 normal samples and 49 nasopharyngeal carcinoma samples downloaded from the Gene Expression Omnibus(GEO) to analysis. R software, STRING, CMap, and various databases were used to screen differentially expressed genes (DEGs), construct the protein-protein interaction (PPI) network, and proceed small molecule compounds analysis, among others.Totally, 424 DEGs were selected from the dataset. DEGs were mainly enriched in extracellular matrix organization, cilium organization, PI3K-Akt signaling pathway, collagen-containing extracellular matrix, and extracellular matrix-receptor interaction, among others. Top 10 upregulated and top 10 downregulated hub genes were identified as hub DEGs. Piperlongumine, apigenin, menadione, 1,4-chrysenequinone, and chrysin were identified as potential drugs to prevent and treat NPC. Besides, the effect of genes CDK1, CDC45, RSPH4A, and ZMYND10 on survival of NPC was validated in GEPIA database.The data revealed novel aberrantly expressed genes and pathways in NPC by bioinformatics analysis, potentially providing novel insights for the molecular mechanisms governing NPC progression. Although further studies needed, the results demonstrated that the expression levels of CDK1, CDC45, RSPH4A, and ZMYND10 probably affected survival of NPC patients.

Low-input ATAC&mRNA-seq protocol for simultaneous profiling of chromatin accessibility and gene expression.

We present a simple, fast, and robust protocol (low-input ATAC&mRNA-seq) to simultaneously generate ATAC-seq and mRNA-seq libraries from the same cells in limited cell numbers by coupling a simplified ATAC procedure using whole cells with a novel mRNA-seq approach that features a seamless on-bead process including direct mRNA isolation from the cell lysate, solid-phase cDNA synthesis, and direct tagmentation of mRNA/cDNA hybrids for library preparation. It enables dual-omics profiling from limited material when joint epigenome and transcriptome analyses are needed. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

Endometrium On-a-Chip Reveals Insulin- and Glucose-induced Alterations in the Transcriptome and Proteomic Secretome.

The molecular interactions between the maternal environment and the developing embryo are key for early pregnancy success and are influenced by factors such as maternal metabolic status. Our understanding of the mechanism(s) through which these individual nutritional stressors alter endometrial function and the in utero environment for early pregnancy success is, however, limited. Here we report, for the first time, the use of an endometrium-on-a-chip microfluidics approach to produce a multicellular endometrium in vitro. Isolated endometrial cells (epithelial and stromal) from the uteri of nonpregnant cows in the early luteal phase (Days 4-7) were seeded in the upper chamber of the device (epithelial cells; 4-6 × 104 cells/mL) and stromal cells seeded in the lower chamber (1.5-2 × 104 cells/mL). Exposure of cells to different concentrations of glucose (0.5, 5.0, or 50 mM) or insulin (Vehicle, 1 or 10 ng/mL) was performed at a flow rate of 1 µL/minute for 72 hours. Quantitative differences in the cellular transcriptome and the secreted proteome of in vitro-derived uterine luminal fluid were determined by RNA-sequencing and tandem mass tagging mass spectrometry, respectively. High glucose concentrations altered 21 and 191 protein-coding genes in epithelial and stromal cells, respectively (P < .05), with a dose-dependent quantitative change in the protein secretome (1 and 23 proteins). Altering insulin concentrations resulted in limited transcriptional changes including transcripts for insulin-like binding proteins that were cell specific but altered the quantitative secretion of 196 proteins. These findings highlight 1 potential mechanism by which changes to maternal glucose and insulin alter uterine function.

Transcriptome comparisons of in vitro intestinal epithelia grown under static and microfluidic gut-on-chip conditions with in vivo human epithelia.

Gut-on-chip devices enable exposure of cells to a continuous flow of culture medium, inducing shear stresses and could thus better recapitulate the in vivo human intestinal environment in an in vitro epithelial model compared to static culture methods. We aimed to study if dynamic culture conditions affect the gene expression of Caco-2 cells cultured statically or dynamically in a gut-on-chip device and how these gene expression patterns compared to that of intestinal segments in vivo. For this we applied whole genome transcriptomics. Dynamic culture conditions led to a total of 5927 differentially expressed genes (3280 upregulated and 2647 downregulated genes) compared to static culture conditions. Gene set enrichment analysis revealed upregulated pathways associated with the immune system, signal transduction and cell growth and death, and downregulated pathways associated with drug metabolism, compound digestion and absorption under dynamic culture conditions. Comparison of the in vitro gene expression data with transcriptome profiles of human in vivo duodenum, jejunum, ileum and colon tissue samples showed similarities in gene expression profiles with intestinal segments. It is concluded that both the static and the dynamic gut-on-chip model are suitable to study human intestinal epithelial responses as an alternative for animal models.

Oncotype DX and Prosigna in breast cancer patients: A comparison study.

BACKGROUND: Oncotype Dx® (ODX) is the most used prognostic and predictive assay for ER + breast cancer (BCa) and is categorized into low (< 18), intermediate (18 to 30), or high (≥31) risk of recurrence. Prosigna® is a prognostic signature to estimate distant recurrence-free survival for stage I/II, ER+ cancer in postmenopausal women treated with adjuvant therapy. The goal of the study is to assess the agreement between ODX and Prosigna®. MATERIALS AND METHODS: 100 previously ODX classified peri and postmenopausal, early-stage (I or II) BCa patients were retrieved and Prosigna assay was performed on archived tumor blocks on a NanoString nCounter® DX Analysis System. RESULTS: ODX assay was assigned as follows: 57% low, 39% intermediate, and 4% high. There were 8% two-step disagreements (high to low or vice versa) between ODX and Prosigna®; and 42% one-step disagreement (low to intermediate or vice versa). 78% were classified by Prosigna as luminal A and 22% as luminal B. The majority of luminal A cases (67/78; 85.9%) had low ROR score whereas ODX classified almost two-thirds (50/78~ 64%) as low RS. An insignificant percentage of luminal B cases (1/22 - 4.5%) were classified as high RS by ODX, and a modest percentage were classified as high ROR by Prosigna (15/22 ~68%). According to our follow up results, recurrence was detected in three cases. In all three cases; Prosigna was a better indicator of recurrence. CONCLUSIONS: The agreement between ODX and Prosigna® is low, and this has management implications, especially when chemotherapy is needed.

STARCH: copy number and clone inference from spatial transcriptomics data.

Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

Become Competent in Generating RNA-Seq Heat Maps in One Day for Novices Without Prior R Experience.

Heat map visualization of RNA-seq data is a commonplace task. However, most laboratories rely on bioinformaticians who are not always available. Biological scientists are afraid to prepare heat maps independently because R is a programming platform. Here, using RNA-seq data for 16 differentially expressed genes in WNT pathway between embryonic stem cells and fibroblasts, I share a tutorial for novices without any prior R experience to master the skills, in one day, required for preparation of heat maps using the pheatmap package. Procedures described include installation of R, RStudio, and the pheatmap package, as well as hands-on practices for some basic R commands, conversion of RNA-seq data frame to a numeric matrix suitable for generation of heat maps, and defining arguments for the pheatmap function to make a desired heat map. More than 20 template scripts are provided to generate heat maps and to control the dimensions and appearances of the heat maps.

Leveraging Automation toward Development of a High-Throughput Gene Expression Profiling Platform.

We previously developed a panel of one-step real-time quantitative reverse transcription PCR (one-step qRT-PCR; hereafter referred to as qRT-PCR) assays to assess compound efficacy. However, these high-cost, conventional qRT-PCR manual assays are not amenable to high-throughput screen (HTS) analysis in a time-sensitive and complex drug discovery process. Here, we report the establishment of an automated gene expression platform using in-house lysis conditions that allows the study of various cell lines, including primary T cells. This process innovation provides the opportunity to perform genotypic profiling in both immunology and oncology therapeutic areas with quantitative studies as part of routine drug discovery program support. This newly instituted platform also enables a panel screening strategy to efficiently connect HTS, lead identification, and lead optimization in parallel.

Digital microfluidic isolation of single cells for -Omics.

We introduce Digital microfluidic Isolation of Single Cells for -Omics (DISCO), a platform that allows users to select particular cells of interest from a limited initial sample size and connects single-cell sequencing data to their immunofluorescence-based phenotypes. Specifically, DISCO combines digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to collect the contents of single cells from heterogeneous populations, followed by analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanoflow liquid chromatography and tandem mass spectrometry. The results described herein confirm the utility of DISCO for sequencing at levels that are equivalent to or enhanced relative to the state of the art, capable of identifying features at the level of single nucleotide variations. The unique levels of selectivity, context, and accountability of DISCO suggest potential utility for deep analysis of any rare cell population with contextual dependencies.

Prediction of prognostic signatures in triple-negative breast cancer based on the differential expression analysis via NanoString nCounter immune panel.

BACKGROUND: Triple-Negative Breast Cancer (TNBC) is an aggressive and complex subtype of breast cancer. The current biomarkers used in the context of breast cancer treatment are highly dependent on the targeting of oestrogen receptor, progesterone receptor, or HER2, resulting in treatment failure and disease recurrence and creating clinical challenges. Thus, there is still a crucial need for the improvement of TNBC treatment; the discovery of effective biomarkers that can be easily translated to the clinics is essential. METHODS: We report an approach for the discovery of biomarkers that can predict tumour relapse and pathologic complete response (pCR) in TNBC on the basis of mRNA expression quantified using the NanoString nCounter Immunology Panel. To overcome the limited sample size, prediction models based on random Forest were constructed using the differentially expressed genes (DEGs) as selected features. We also evaluated the differences between pre- and post-treatment groups aiming for the combinatorial assessment of pCR and relapse using additive models in edgeR. RESULTS: We identify nine and 13 DEGs strongly associated with pCR and relapse, respectively, from 579 immune genes in a small number of samples (n = 55) using edgeR. An additive model for the comparison of pre- and post-treatment groups via the adjustment of the independent subject in the relapse group revealed associations for 41 genes. Comprehensive analysis indicated that our prediction models outperformed those constructed using features extracted from the existing feature selection model Elastic Net in terms of accuracy. The prediction models were assessed using a randomization test to validate the robustness (empirical P for the model of pCR = 0.015 and empirical P for the model of relapse = 0.018). Furthermore, three DEGs (FCER1A, EDNRB, and TGFBI) in the model of relapse showed prognostic significance for predicting the survival of patients with cancer through Cox proportional hazards regression model-based survival analysis. CONCLUSION: Gene expression quantified via the NanoString nCounter Immunology Panel can be seamlessly analysed using edgeR, even considering small sample sizes. Our approach provides a scalable framework that can easily be applied for the discovery of biomarkers based on the NanoString nCounter Immunology Panel. DATA AVAILABILITY: The source code will be available from github at https://github.com/sungheep/nanostring .

Health economic impact of a biopsy-based cell cycle gene expression assay in localized prostate cancer.

Background: Prior studies have established that broader incorporation of active surveillance, guided by additional prognostic tools, may mitigate the growing economic burden of localized prostate cancer in the USA. This study sought to further explore the potential of a particular gene expression-based prognostic tool to address this unmet need. Materials & methods: A deterministic, decision-analytic model was developed to estimate the economic impact of the Prolaris® test on a US commercial health plan. Results & conclusion: When adopted in patients classified by the American Urological Association as low or intermediate risk, the assay was projected to reduce costs by $1894 and $2129 per patient over 3 and 10 years, respectively, largely through the increased use of active surveillance.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine.

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Performance of Gene Expression Profile Tests for Prognosis in Patients With Localized Cutaneous Melanoma: A Systematic Review and Meta-analysis.

Importance: The performance of prognostic gene expression profile (GEP) tests for cutaneous melanoma is poorly characterized. Objective: To systematically assess the performance of commercially available GEP tests in patients with American Joint Committee on Cancer (AJCC) stage I or stage II disease. Data Sources: For this systematic review and meta-analysis, comprehensive searches of PubMed/MEDLINE, Embase, and Web of Science were conducted on December 12, 2019, for English-language studies of humans without date restrictions. Study Selection: Two reviewers identified GEP external validation studies of patients with localized melanoma. After exclusion criteria were applied, 7 studies (8%; 5 assessing DecisionDx-Melanoma and 2 assessing MelaGenix) were included. Data Extraction and Synthesis: Data were extracted using an adaptation of the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS-PF). When feasible, meta-analysis using random-effects models was performed. Risk of bias and level of evidence were assessed with the Quality in Prognosis Studies tool and an adaptation of Grading of Recommendations Assessment, Development, and Evaluation. Main Outcomes and Measures: Proportion of patients with or without melanoma recurrence correctly classified by the GEP test as being at high or low risk. Results: In the 7 included studies, a total of 1450 study participants contributed data (age and sex unknown). The performance of both GEP tests varied by AJCC stage. Of patients tested with DecisionDx-Melanoma, 623 had stage I disease (6 true-positive [TP], 15 false-negative, 61 false-positive, and 541 true-negative [TN] results) and 212 had stage II disease (59 TP, 13 FN, 78 FP, and 62 TN results). Among patients with recurrence, DecisionDx-Melanoma correctly classified 29% with stage I disease and 82% with stage II disease. Among patients without recurrence, the test correctly classified 90% with stage I disease and 44% with stage II disease. Of patients tested with MelaGenix, 88 had stage I disease (7 TP, 15 FN, 15 FP, and 51 TN results) and 245 had stage II disease (59 TP, 19 FN, 95 FP, and 72 TN results). Among patients with recurrence, MelaGenix correctly classified 32% with stage I disease and 76% with stage II disease. Among patients without recurrence, the test correctly classified 77% with stage I disease and 43% with stage II disease. Conclusions and Relevance: The prognostic ability of GEP tests among patients with localized melanoma varied by AJCC stage and appeared to be poor at correctly identifying recurrence in patients with stage I disease, suggesting limited potential for clinical utility in these patients.

Integrated miRNA-mRNA network revealing the key molecular characteristics of ossification of the posterior longitudinal ligament.

BACKGROUND: Ossification of the posterior longitudinal ligament (OPLL) refers to an ectopic ossification disease originating from the posterior longitudinal ligament of the spine. Pressing on the spinal cord or nerve roots can cause limb sensory and motor disorders, significantly reducing the patient's quality of life. At present, the pathogenesis of OPLL is still unclear. The purpose of this study is to integrate microRNA (miRNA)-mRNA biological information data to further analyze the important molecules in the pathogenesis of OPLL, so as to provide targets for future OPLL molecular therapy. METHODS: miRNA and mRNA expression profiles of GSE69787 were downloaded from Gene Expression Omnibus database and analyzed by edge R package. Funrich software was used to predict the target genes and transcription factors of de-miRNA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentially expressed genes (DEGs) were carried out based on CLUEGO plug-in in Cytoscape. Using data collected from a search tool for the retrieval of interacting genes online database, a protein-protein interaction (PPI) network was constructed using Cytoscape. The hub gene selection and module analysis of PPI network were carried out by cytoHubba and molecular complex detection, plug-ins of Cytoscape software respectively. RESULTS: A total of 346 genes, including 247 up-regulated genes and 99 down-regulated genes were selected as DEGs. SP1 was identified as an upstream transcription factor of de-miRNAs. Notably, gene ontology enrichment analysis shows that up- and down-regulated DEGs are mainly involved in BP, such as skeletal structure morphogenesis, skeletal system development, and animal organ morphogenesis. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that only WNT signaling pathway was associated with osteogenic differentiation. Lymphoid enhancer binding factor 1 and wingless-type MMTV integration site family member 2 Wingless-Type MMTV Integration site family member 2 were identified as hub genes, miR-520d-3p, miR-4782-3p, miR-6766-3p, and miR-199b-5p were identified as key miRNAs. In addition, 2 important network modules were obtained from PPI network. CONCLUSIONS: In this study, we established a potential miRNA-mRNA regulatory network associated with OPLL, revealing the key molecular mechanism of OPLL and providing targets for future treatment or prevent its occurrence.

Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis were used to get the enrichment trend of DEGs of GSE53757 and GSE16449. Draw Venn Diagram was applied for co-expression of DEGs. Cytoscape with the Retrieval of Interacting Gene (STRING) datasets and Molecular Complex Detection (MCODE) were performed protein-protein interaction (PPI) of DEGs. The Kaplan-Meier Plotter analysis of top 15 upregulated and top 15 downregulated were selected in Gene Expression Profiling Interactive Analysis (GEPIA). Then, the expression level of hub genes between normal renal tissue and different pathological stages of ccRCC tissue, which significantly correlated with overall survival in ccRCC patients, were also analyzed by Ualcan based on The Cancer Genome Atlas (TCGA) database. In this study, we got 167 co-expression DEGs, including 72 upregulated DEGs and 95 downregulated DEGs. We identified 11 hub genes had significantly correlated with overall survival in ccRCC patients. Among them, KIF23, APLN, ADCY1, GREB1, TLR4, IRF8, CXCL1, CXCL2, deserved our attention.