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Identification of Candidate Biomarkers and Prognostic Analysis In ORIGINAL RESEARCH published: 04 August 2021 doi: 10.3389/fonc.2021.652354 Identification of Candidate Biomarkers and Prognostic Analysis in Colorectal Cancer Liver Metastases † † Tianhao Zhang , Kaitao Yuan , Yingzhao Wang, Mingze Xu, Shirong Cai, Chuangqi Chen and Jinping Ma* Division of Gastrointestinal Surgery Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Background: Colorectal cancer (CRC), one of the most common malignant tumors worldwide, has a high mortality rate, especially for patients with CRC liver metastasis (CLM). However, CLM pathogenesis remains unclear. Methods: We integrated multiple cohort datasets and databases to clarify and verify Edited by: potential key candidate biomarkers and signal transduction pathways in CLM. GEO2R, Zsolt Kova´ cs, DAVID 6.8, ImageGP, STRING, UALCAN, ONCOMINE, THE HUMAN PROTEIN ATLAS, George Emil Palade University of Medicine, Pharmacy, Sciences and GEPIA 2.0, cBioPortal, TIMER 2.0, DRUGSURV, CRN, GSEA 4.0.3, FUNRICH 3.1.3 and Technology of Taˆ rgu Mures¸, Romania R 4.0.3 were utilized in this study. Reviewed by: Results: Sixty-three pairs of matched colorectal primary cancer and liver metastatic gene Tomas Kazda, Masaryk Memorial Cancer Institute expression profiles were screened from three gene expression profiles (GSE6988, (MMCI), Czechia GSE14297 and GSE81558). Thirty-one up-regulated genes and four down-regulated Louise Catherine Connell, fi fi fi Cornell University, United States genes were identi ed from these three gene expression pro les and veri ed by another fi *Correspondence: gene expression pro les (GSE 49355) and TCGA database. Two pathways (IGFBP-IGF Jinping Ma signaling pathway and complement-coagulation cascade), eighteen key differentially [email protected] expressed genes (DEGs), six hub genes (SPARCL1, CDH2, CP, HP, TF and † These authors share first authorship SERPINA5) and two biomarkers (CDH2 and SPARCL1) with significantly prognostic fi Specialty section: values were screened by multi-omics data analysis and veri ed by Gene Expression This article was submitted to Omnibus (GEO) and The Cancer Genome Atlas (TCGA) cohort. Gastrointestinal Cancers, fi a section of the journal Conclusions: In this study, we identi ed a robust set of potential candidate biomarkers in Frontiers in Oncology CLM, which would provide potential value for early diagnosis and prognosis, and would Received: 12 January 2021 promote molecular targeting therapy for CRC and CLM. Accepted: 19 July 2021 Published: 04 August 2021 Keywords: colorectal cancer, differentially expressed genes, liver metastasis, biomarkers, prognostic analysis Citation: Zhang T, Yuan K, Wang Y, Xu M, Cai S, Chen C and Ma J (2021) Abbreviations: AI, Artificial Intelligence; CLM, Colorectal Cancer Liver Metastasis; COAD, Colon Adenocarcinoma; CRC, Identification of Candidate Biomarkers Colorectal Cancer; DEG, Differentially Expressed Genes; DFS, Disease-Free Survival; GEO, Gene Expression Omnibus; GO, and Prognostic Analysis in Colorectal Gene Ontology; GSEA, Gene Set Enrichment Analysis; IGF, Insulin-like Growth Factor; IGFBPs, Insulin-like Growth Factor- Cancer Liver Metastases. Binding Protein 1; KEGG, Kyoto Encyclopedia of Genes and Genomes; LIHC, Liver Hepatocellular Carcinoma; NGS, Next Front. Oncol. 11:652354. Generation Sequence; OS, Overall Survival; PPI, Protein-protein Interaction Network; READ, Rectal Adenocarcinoma; TCGA, doi: 10.3389/fonc.2021.652354 The Cancer Genome Atlas; TME, Tumor Microenvironment; TIMER, Tumor Immune Estimation Resource. Frontiers in Oncology | www.frontiersin.org 1 August 2021 | Volume 11 | Article 652354 Zhang et al. Biomarkers in Colorectal Cancer INTRODUCTION MATERIALS AND METHODS Colorectal cancer (CRC) is one of the most common malignant NCBI-GEO tumors worldwide. According to global cancer statistics, more than NCBI-GEO (Gene Expression Omnibus) (https://www.ncbi.nlm. 1.9 million new CRC cases and 935,000 deaths were reported in nih.gov/geo) is a free microarray/gene profile and NGS database. In 2020, accounting for approximately one-tenth of cancer cases and our study, we screened GSE6988 (5), GSE14297 (6), and GSE81558 deaths. Overall, the incidence of CRC ranks third globally, and the (7) microarray datasets containing expression profiles of matching mortality rate ranks second (1). Colorectal cancer liver metastasis colorectal liver metastases (primary colon cancer samples and liver (CLM) is one of the primary causes of this high mortality rate, metastasis samples of the same patient), and 63 pairs of matching which occurs in 30% of CRC patients, accounting for two-thirds of primary CRC and liver metastatic cancer tissues were used. To the related deaths (2). Further, more than 50% of patients relapse confirm the reliability of DEGs identified from the three GSE within 2 years after CLM resection (3). datasets, this study selected the GSE49355 (8) microarray dataset, Numerous clinical data indicate that the liver is the most which includes 13 pairs of matched primary colorectal cancer and common target organ for CRC metastasis. To date, the relevant liver metastasis samples, in the GEO database for verification. mechanisms underlying the formation and progression of liver metastasis during disease progression of this disease have been GEO2R extensively studied, however, the pathogenesis has not been fully GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/), is a data elucidated. With the rise of emerging technologies such as genome processing tool on GEO. Statistically significant differences were and transcriptome sequencing, gene-editing technology, and identified based on a classic t-test, considering p < 0.05 and artificial intelligence (AI), biomedical research is undergoing |log FC| > 1 as the cut-off criteria. In this study, we used GEO2R revolutionary changes, gradually transforming from traditional to filter the original data to determine DEGs and visualized them with medicine to precision medicine. Among them, next-generation SANGERBOX (http://sangerbox.com/Index), FUNRICH and R. sequencing (NGS) technology has revolutionized our ability to obtain information from the genome regarding the DNA DAVID sequence itself, as well as the state of the transcriptome and the Database for Annotation, Visualization and Integrated Discovery epigenome (4). However, most NGS technologies have not solved (DAVID) 6.8 (https://david.ncifcrf.gov/home.jsp) is a the functional interpretation of differentially expressed genes comprehensive, functional annotation website that help (DEGs) to identify appropriate key genes. Indeed, the occurrence investigators better clarify the biological function of submitted and development of CLM involves a myriad of epigenetic and genes (9). In our study, the Gene Ontology (GO) enrichment genetic changes within multiple functional signaling pathways. analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) These different networks are susceptible to regulation by genetic pathway enrichment analysis of DEGs were isolated from DAVID and epigenetic events, leading to diversity in the expression profiles. 6.8 and visualized with EHbio (http://www.ehbio.com/ImageGP/ Therefore, the combination of integrated bioinformatics methods index.php/home/index/scatterplot.html). Biological processes and expression profiling technology may have the ability to (BP), cellular components (CC) and molecular function (MF) overcome, thus screening suitable biomarkers and guiding the were included in GO enrichment analysis. selection of clinical systemic prevention, diagnostic, and treatment options. STRING In this study, we aimed to analyze and predict candidate STRING (https://string-db.org/) aims to collect, score, and integrate biomarkers of CLM. Firstly, key DEGs were screened from gene all publicly available sources of protein-protein interaction (PPI) expression profiles of GEO database (Gene Expression Omnibus). data and complement these with computational predictions of Specifically, we identified the biological functions and signal potential functions (10). We used STRING to develop and transduction pathways of the selected DEGs through GO (Gene construct DEG-encoded proteins and PPI networks and analyze ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) the interactions among candidate DEG-encoded proteins, and we enrichment analysis. Further, we constructed protein-protein visualized them with CYTOSCAPE 3.7.2. interaction (PPI) networks and analyzed prognostic values of candidate genes in CRC through data mining. Finally, to evaluate UALCAN whether the selected candidate biomarkers are reliable, we analyzed UALCAN (http://ualcan.path.uab.edu/analysis.html), a thegeneexpressionprofile data sets, RNA-Seq data sets of GEO and comprehensive web resource, provides analyses based on The TCGA databases, and used CNV (copy number variation), GESA Cancer Genome Atlas (TCGA) and MET500 cohort data (11). In (Gene Set Enrichment Analysis), IHC (immunohistochemistry), this study, we used UALCAN to analyze the expression profiles and other methods for verification. Moreover, the verification and prognostic values of DEGs. Student’s t-test was used to results are basically consistent with our research conclusions. generate a p-value. The p-value cutoff was 0.05. Therefore, this study will contribute to understanding the molecular mechanism in depth and contribute to the discovery of ONCOMINE new appropriate molecular diagnostic and therapeutic targets, and ONCOMINE (www.oncomine.org), currently the world’s largest more accurately prognose long term outcome
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