Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

Development and validation of a 14-gene signature for prognosis prediction in hepatocellular carcinoma

Worldwide, hepatocellular carcinoma (HCC) remains a crucial medical problem. Precise and concise prognostic models are urgently needed because of the intricate gene variations among liver cancer cells. We conducted this study to identify a prognostic gene signature with biological significance. We applied two algorithms to generate differentially expressed genes (DEGs) between HCC and normal specimens in The Cancer Genome Atlas cohort (training set included) and performed enrichment analyses to expound on their biological significance. A protein-protein interactions network was established based on the STRING online tool. We then used Cytoscape to screen hub genes in crucial modules. A multigene signature was constructed by Cox regression analysis of hub genes to stratify the prognoses of HCC patients in the training set. The prognostic value of the multigene signature was externally validated in two other sets from Gene Expression Omnibus (GSE14520 and GSE76427), and its role in recurrence prediction was also investigated. A total of 2000 DEGs were obtained, including 1542 upregulated genes and 458 downregulated genes. Subsequently, we constructed a 14-gene signature on the basis of 56 hub genes, which was a good predictor of overall survival. The prognostic signature could be replicated in GSE14520 and GSE76427. Moreover, the 14-gene signature could be applied for recurrence prediction in the training set and GSE14520. In summary, the 14-gene signature extracted from hub genes was involved in some of the HCC-related signalling pathways; it not only served as a predictive signature for HCC outcome but could also be used to predict HCC recurrence.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

microRNA-329 suppresses epithelial-to-mesenchymal transition and lymph node metastasis in bile duct cancer by inhibiting laminin subunit beta 3

Bile duct cancer (BDC), also known as cholangiocarcinoma, is a highly desmoplastic cancer with a growth pattern characterized by periductal extension and infiltration. Studies have suggested that microRNAs (miRNAs) play an important role in BDC progression. Here we aim at investigating the effects of miR-329 on BDC development, focusing especially on epithelial-to-mesenchymal transition (EMT) in vitro and lymph node metastasis in vivo. Expression microarrays associated with BDC tissues were collected and differentially expressed genes were analyzed, followed by miRNA target prediction and verification. The role miR-329 played in BDC was examined using gain-of-function and loss-of-function methods. The expressions of miR-329, laminin subunit beta 3 (LAMB3), and EMT markers, in addition to cell proliferation, migration, and invasion were evaluated. Furthermore, nude mice models of BDC were established to observe tumor growth and metastatic lymph nodes. The LAMB3 was identified as an upregulated gene based on the GSE77984 and GSE45001 microarray analysis. LAMB3 was also predicted and confirmed to be a target gene of miR-329 by dual-luciferase reporter assay. Through further cell experiments, the EMT process was reversed, cell proliferation, invasion, and migration were suppressed, when miR-329 was upregulated. Furthermore, in vivo experiments exhibited that the overexpression of miR-329 inhibited tumor growth and the number of metastatic lymph nodes. This study provides in vivo and in vitro evidence that miR-329 inhibits BDC progression through translational repression of LAMB3. Therefore, the obtained results may aid as an experimental basis for improving prognosis of BDC.     

Genome-wide 5-Hydroxymethylcytosine Profiling Analysis Identifies MAP7D1 as A Novel Regulator of Lymph Node Metastasis in Breast Cancer

Although DNA 5-hydroxymethylcytosine(5 hmC) is recognized as an important epigenetic mark in cancer, its precise role in lymph node metastasis remains elusive. In this study, we investigated how 5 hmC associates with lymph node metastasis in breast cancer. Accompanying with high expression of TET1 and TET2 proteins, large numbers of genes in the metastasis-positive primary tumors exhibit higher 5 hmC levels than those in the metastasis-negative primary tumors. In contrast, the TET protein expression and DNA 5 hmC decrease significantly within the metastatic lesions in the lymph nodes compared to those in their matched primary tumors. Through genomewide analysis of 8 sets of primary tumors, we identified 100 high-confidence metastasis-associated5 hmC signatures, and it is found that increased levels of DNA 5 hmC and gene expression of MAP7 D1 associate with high risk of lymph node metastasis. Furthermore, we demonstrate that MAP7 D1, regulated by TET1, promotes tumor growth and metastasis. In conclusion, the dynamic5 hmC profiles during lymph node metastasis suggest a link between DNA 5 hmC and lymph node metastasis. Meanwhile, the role of MAP7 D1 in breast cancer progression suggests that the metastasis-associated 5 hmC signatures are potential biomarkers to predict the risk for lymph node metastasis, which may serve as diagnostic and therapeutic targets for metastatic breast cancer.     

间质表皮转化因子通过PI3K/AKT/MMPs信号通路促进肺腺癌细胞的侵袭转移

间质表皮转化因子（mesenchymal to epithelial transition factor,MET）在多种癌症中异常表达,影响肿瘤的发生发展,但MET影响肺腺癌的分子机制并不明确。本研究收集3例淋巴结转移的肺腺癌组织（lung adenocarcinoma tissues,LAD）和3例无淋巴结转移的肺腺癌组织,用于微阵列基因芯片分析。结果显示,与无淋巴结转移的肺腺癌组织相比,有淋巴结转移的肺腺癌组织中有1 314条mRNAs表达上调,400条mRNAs表达下调。其中,MET在有淋巴结转移的肺腺癌组织中表达显著升高。随机选取8个差异表达基因,对收集的潍坊医学院附属医院2014年2月至2017年2月间有淋巴结转移的肺腺癌组织和无淋巴结转移的肺腺癌组织各30例通过qRT -PCR实验进行微阵列基因芯片验证。结果显示,所选mRNAs的表达与微阵列结果一致,验证了微阵列基因芯片结果的准确性。通过Western 印迹进一步检测MET的表达。结果显示,相较于正常肺上皮细胞,肺腺癌细胞中MET的表达显著升高。利用质粒转染,敲减肺腺癌细胞A549中的MET,Transwell侵袭实验结果显示,敲减MET后肺腺癌细胞的侵袭能力明显降低;对各细胞组进行EGF（epidermal growth factor）处理并检测PI3K/AKT/MMPs信号通路,Western 印迹检测结果显示,敲减MET后,肺腺癌细胞中基质金属蛋白酶-2（matrix metalloproteinase 2,MMP-2）和MMP-9的表达显著下降, AKT的磷酸化水平也显著下降。上述结果表明,MET可通过激活PI3K/AKT信号通路进而增加MMPs的表达促进肺腺癌的侵袭转移。     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

Background: The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy.Materials and methods: We identified differential genes (DEGs) that were up- and down-regulated in the three datasets ({"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942, {"type":"entrez-geo","attrs":{"text":"GSE13911","term_id":"13911"}}GSE13911, and {"type":"entrez-geo","attrs":{"text":"GSE118916","term_id":"118916"}}GSE118916) and created protein–protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment.Results: Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan–Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy.Conclusion: ECM–receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC patients.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

Identification and Characterization of Cadmium-Related Genes in Liver Carcinoma

Evidence indicates that exposure to heavy trace element might be a risk factor for liver carcinoma. Cadmium has been supposed to be a carcinogen that has a correlation with the risk of a number of cancers, including liver cancer. However, the mechanisms underlying Cadmium-induced malignant transformation in liver cells are not fully understood. In the present study, we aimed to screen the differentially expressed genes (DEGs) that might play a role in both the Cadmium-related liver cell transformation and the development of liver cancer. Microarray-based gene expression profiles concerning liver carcinoma vs non-cancerous tissue (GSE64041) and Cadmium-treated liver cells vs controls (GSE8865 and GSE31286), respectively, were retrieved from Gene Expression Omnibus (GEO) database. Then, DEGs of each profile were calculated and screened. The intersection of each DEGs was obtained by Venn analysis. Afterwards, the possible roles of the selected genes in cancer development were evaluated by using Oncomine database and TCGA cohort analysis. Consequently, three DEGs, LRAT, SLC7A11, and ITGA2, were selected for further analysis. SLC7A11 and ITGA2, but not LRAT, were upregulated in liver cancer compared with those in normal tissues, respectively. After using a TCGA cohort analysis, results failed to show a significant correlation between SLC7A11 or ITGA2 expression and clinical parameters. However, the survival analysis showed that patients with high expression levels of SLC7A11 had a shorter overall survival time relative to those of the patients with low levels. In conclusion, SLC7A11 and ITGA2 might play a role in the Cadmium-induced liver cell damage or transformation, and the development of liver carcinoma. SLC7A11 might be a prognostic factor for patients with liver carcinoma. Future validation experiments are needed to verify the results.     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Identification of potential genes related to breast cancer brain metastasis in breast cancer patients

Brain metastases (BMs) usually develop in breast cancer (BC) patients. Thus, the molecular mechanisms of breast cancer brain metastasis (BCBM) are of great importance in designing therapeutic strategies to treat or prevent BCBM. The present study attempted to identify novel diagnostic and prognostic biomarkers of BCBM. Two datasets ({"type":"entrez-geo","attrs":{"text":"GSE125989","term_id":"125989"}}GSE125989 and {"type":"entrez-geo","attrs":{"text":"GSE100534","term_id":"100534"}}GSE100534) were obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) in cases of BC with and without brain metastasis (BM). A total of 146 overlapping DEGs, including 103 up-regulated and 43 down-regulated genes, were identified. Functional enrichment analysis showed that these DEGs were mainly enriched for functions including extracellular matrix (ECM) organization and collagen catabolic fibril organization. Using protein–protein interaction (PPI) and principal component analysis (PCA) analysis, we identified ten key genes, including LAMA4, COL1A1, COL5A2, COL3A1, COL4A1, COL5A1, COL5A3, COL6A3, COL6A2, and COL6A1. Additionally, COL5A1, COL4A1, COL1A1, COL6A1, COL6A2, and COL6A3 were significantly associated with the overall survival of BC patients. Furthermore, COL6A3, COL5A1, and COL4A1 were potentially correlated with BCBM in human epidermal growth factor 2 (HER2) expression. Additionally, the miR-29 family might participate in the process of metastasis by modulating the cancer microenvironment. Based on datasets in the GEO database, several DEGs have been identified as playing potentially important roles in BCBM in BC patients.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Restoration of miR-29b exerts anti-cancer effects on glioblastoma

Background

Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.

Methods

Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.

Results

Totally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine–cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell–cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.

Conclusion

In summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.