Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Cardioprotective role of oleanolic acid in patients with type 2 diabetes mellitus

Background

Patients with type 2 diabetes mellitus (T2DM) experience a decline in cardiac function, resulting in poor prognosis. Therefore, restoration of cardiac function and improvement of myocardial fibrosis is an important treatment goal for patients with T2DM.

Material and methods

The chemical structure of oleanolic acid(OA) was downloaded from PubChem and uploaded to PharmMapper. GeneCards and OMIM databases were searched for genes related to OA and disease and plotted into a Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using R software. Then, a mouse model of diabetes mellitus was established, and ELISA, echocardiographic analysis of cardiac function, TUNEL assay, and reactive oxygen species assay were performed.

Results

Network pharmacology analysis identified the related targets and potential molecular mechanisms underlying the effects of OA in T2DM. ELISA, echocardiographic analysis of cardiac function, and TUNEL assay results showed that OA inhibits apoptosis and improves apoptotic indexes in mice with T2DM-induced myocardial injury.

Conclusion

The results demonstrate the myocardial protective effect of OA in this mouse model.

Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer

Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

Amphiregulin blockade decreases the levodopa-induced dyskinesia in a 6-hydroxydopamine Parkinson's disease mouse model

BACKGROUND

Levodopa (L-DOPA) is considered the most reliable drug for treating Parkinson's disease (PD) clinical symptoms. Regrettably, long-term L-DOPA therapy results in the emergence of drug-induced abnormal involuntary movements (AIMs) in most PD patients. The mechanisms underlying motor fluctuations and dyskinesia induced by L-DOPA (LID) are still perplexing.

METHODS

Here, we first performed the analysis on the microarray data set (GSE55096) from the gene expression omnibus (GEO) repository and identified the differentially expressed genes (DEGs) using linear models for microarray analysis (Limma) R packages from the Bioconductor project. 12 genes (Nr4a2, Areg, Tinf2, Ptgs2, Pdlim1, Tes, Irf6, Tgfb1, Serpinb2, Lipg, Creb3l1, Lypd1) were found to be upregulated. Six genes were validated on quantitative polymerase chain reaction and subsequently, Amphiregulin (Areg) was selected (based on log2 fold change) for further experiments to unravel its involvement in LID. Areg LV_shRNA was used to knock down Areg to explore its therapeutic role in the LID model.

RESULTS

Western blotting and immunofluorescence results show that AREG is significantly expressed in the LID group relative to the control. Dyskinetic movements in LID mice were alleviated by Areg knockdown, and the protein expression of delta FOSB, the commonly attributable protein in LID, was decreased. Moreover, Areg knockdown reduced the protein expression of P-ERK. In order to ascertain whether the inhibition of the ERK pathway (a common pathway known to mediate levodopa-induced dyskinesia) could also impede Areg, the animals were injected with an ERK inhibitor (PD98059). Afterward, the AIMs, AREG, and ERK protein expression were measured relative to the control group. A group treated with ERK inhibitor had a significant decrease of AREG and phosphorylated ERK protein expression relative to the control group.

CONCLUSION

Taken together, our results indicate unequivocal involvement of Areg in levodopa-induced dyskinesia, thus a target for therapy development.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

Prognostic value and immune-infiltration pattern of FOXD3-AS1 in patients with glioma

Gliomas are difficult-to-treat brain tumors due to their aggressive nature, rapid proliferation, and high invasiveness (Zhang et al., J Cell Biochem, 2019, 120 (9), 15106–15118; Ge et al., Int J Biochem Cell Biol, 2021, 139, 106054). FOXD3-AS1 has been identified as an emerging potential target for tumor prediction and treatment in many studies (Qin et al., Front Oncol, 2021, 11, 688027). However, the utility of FOXD3-AS1 has not been reported in glioma patients (Li et al., Cancer Manag Res, 2021, 13, 9037–9048). The differential profiles of FOXD3-AS1 in TCGA–GBMLGG database were analyzed across clinical subgroups. The analysis of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) revealed that a high level of FOXD3-AS1 was associated with a poor prognosis and survival outcome. Based on the Cox regression analysis, FOXD3-AS1 was found to be a high-risk factor for glioma that affects prognosis outcomes independently. More importantly, because oxidative stress is closely linked to glioma prognosis, we focused on the potential mechanisms of six oxidative stress co-expressed genes with FOXD3-AS1. In addition, the predictive value of FOXD3-AS1 was determined for each clinical subgroup status. The ROC curve results showed that FOXD3-AS1 had a good predictive performance. A stratified clinicopathological subgroup analysis revealed that high expression of FOXD3-AS1 is associated with a poor prognosis. This also indicates a link between FOXD3-AS1 and tumorigenesis and prognosis, which has potential application value. Furthermore, the immune cell infiltration of FOXD3-AS1 and the signal marker correlation suggested that immune cell infiltration differed significantly between immune cell subsets. To the best of our knowledge, this is the first report to investigate FOXD3-AS1 in glioma and how it may modulate GBM and LGG immune microenvironments. Furthermore, FOXD3-AS1 was detected in tumor and paraneoplastic tissues using RT–qPCR. Transwell analysis verified the migration and invasion of the FOXD3-AS1 knockout group in vitro to a certain extent. In conclusion, FOXD3-AS1 can be used as a prognostic indicator for GBM and LGG, and it is closely related to immune infiltration and response to oxidative stress, which may contribute to the advancement of glioma immunotherapy research.

Novel biomarkers for neoplastic progression from ulcerative colitis to colorectal cancer: a systems biology approach

In recent studies, the void of evaluation and in-depth understanding of unknown clinically relevant potential molecular biomarkers involved in colorectal cancer (CRC) from the inflammatory stage of ulcerative colitis (UC) to CRC metastasis, which can be suitable therapeutic targets, is deeply felt. The regulation and interaction among different cancer-promoting molecules, including messenger RNAs (mRNAs) and micro RNAs (miRNAs) in CRC and its progression, were the aim we pursued in this study. Using microarray data, we investigated the differential expression for five datasets, including mRNA and microRNA samples related to UC, tumor/normal. Then, using robust data analysis, separate lists of differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) were identified, which were used for robust rank aggregation (RRA) and co-expression network analysis. Then, comprehensive computational systems biology analyses, including gene ontology and Kyoto encyclopedia of genes and genomic pathway enrichment analyses, mRNA-miRNA regulatory network, and survival analysis, were employed to achieve the aim of this study. Finally, we used clinical samples to validate this potential and new target. According to this systems biology approach, a total of 98 DEGs and 8 DEmiRNAs with common differential expression were identified. By combining the distinct results of RRA and network, several potential therapeutic targets, and predictive and prognostic biomarkers for UC and CRC were identified. These targets include six common hub genes, CXCL1, CXCL8, MMP7, SLCA16A9, PLAU, and TIMP1, which are upregulated. Among these, the important and new biomarker SLC16A9 is negatively regulated by hsa-mir-194-5p, and hsa-miR-378a-5p take. The findings of the present study provide new insight into the pathogenesis of CRC in UC. Our study suggests future evaluation of the functional role of SLC16A9 and hsa-mir-194-5p and hsa-miR-378a-5p in CRC development.

Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning

Intervertebral disc degeneration (IDD), which is distinguished by a variety of pathologic alterations, is the major cause of low back pain (LBP). Nonetheless, preventative measures or therapies that may delay IDD are scarcely available. In this study, we sought to identify new diagnostic biological markers for IDD. In this first-of-a-kind study combining machine learning, stem cell treatment samples and single-cell sequencing data were collected. Differentially expressed genes (DEGs) were detected from the treatment group and clusters. To filter potential markers, support vector machine analysis and LASSO were performed. LAPTM5 was found to be the hub gene for IDD. In addition, the results of single-cell sequencing demonstrated the critical function of stem cells in IDD. Finally, we found that aging is significantly associated with the rate of stem cells. In general, our results may offer fresh insights that may be used in the investigation of innovative markers for diagnosing IDD. The critical genes identified by the machine learning algorithm could provide new perspectives on IDD.

Identification of Novel Hypoxia Subtypes for Prognosis Based on Machine Learning Algorithms

Objective

A reduced level or tension or the deprivation of oxygen is termed hypoxia. It is common for tumours to outgrow their natural source of nutrients, which causes hypoxia in some tumour regions. Hypoxia affects ovarian cancer (OC) in several ways.

Methods

In this study, the expression patterns of prognostic hypoxia-related genes were curated, and consensus clustering analyses were performed to determine hypoxia subtypes in OC included in The Cancer Genome Atlas cohort. Two hypoxia-related subtypes were observed and considered for further investigation. The analyses of differentially expressed genes (DEGs), gene ontology, mutation, and immune cell infraction were performed to explore the underlying molecular mechanisms.

Results

In total, 377 patients with OC were classified into two subgroups based on the subtype of hypoxia. The clinical outcome was considerably poor for patients with hypoxia subtype 2. DEG and protein-protein interaction analyses revealed that the expression levels of CLIP2 and SH3PXD2A were low in OC tissues. Immune cell infarction analysis revealed that the subtypes were associated with the tumour microenvironment (TME).

Conclusion

Our findings established the existence of two distinctive, complex, and varied hypoxia subtypes in OC. Findings from the quantitative analysis of hypoxia subtypes in patients improved our understanding of the characteristics of the TME and may facilitate the development of more efficient treatment regimens.

Collagen type Ⅳ alpha 1 (COL4A1) silence hampers the invasion, migration and epithelial-mesenchymal transition (EMT) of gastric cancer cells through blocking Hedgehog signaling pathway

Gastric cancer (GC), which features high prevalence and mortality rate, remains the third most lethal cancer worldwide. The paper was designed to explore the impacts of collagen type Ⅳ alpha 1 (COL4A1) on GC, along with its potential mechanism. The mRNA and protein expressions of COL4A1 in GC cells were assessed by RT-qPCR and western blot. After depleting COL4A1, RT-qPCR and western blot were conducted again to check the transfection efficacy. With the application of CCK-8, wound healing and transwell, the capabilities of cells to proliferate, migrate and invade were appraised, respectively. Moreover, western blot tested the protein levels of factors involved in migration, proliferation, epithelial mesenchymal transition (EMT) and Hedgehog signaling. As a result, COL4A1 displayed elevated expression in GC tissues and cells while its knockdown inhibited the cell viability, migration, invasion and EMT in GC. According to Gene Set Enrichment Analysis (GSEA), COL4A1 was involved in the regulation of Hedgehog signaling pathway, which was then further verified by the detection of Hedgehog-related proteins. To figure out the relationship between COL4A1 and Hedgehog signaling pathway, we used purmorphamine, an agonist of Hedgehog, to treat GC cells, finding that COL4A1 blocked Hedgehog signaling to inhibit the aggressive phenotypes of GC cells. In short, COL4A1 silence was testified to exhibit suppressive effects on the malignant process of GC, suggesting that COL4A1 might be a potent hallmark for GC therapy.

High expression of oncogene cadherin-6 correlates with tumor progression and a poor prognosis in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common and fatal cancers worldwide. Effective biomarkers to aid the early diagnosis of GC, as well as predict the course of disease, are urgently needed. Hence, we explored the role and function of cadherin-6 (CDH6) in the diagnosis and prognosis of gastric cancer.

METHODS

The expression levels of CDH6 in cancerous and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas (TCGA) dataset. The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and CDH6 expression were evaluated statistically, and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. 44 GC tissue samples, 20 donor-matched adjacent normal tissue samples, and associated detailed clinical information, were collected from the Tianjin Medical University General Hospital. CDH6 expression levels were determined for further validation.

RESULTS

CDH6 was upregulated in GC samples compared to normal gastric tissue. Furthermore, GSEA identified the tricarboxylic acid (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism, oxidative phosphorylation, and the pentose phosphate pathway as differentially enriched in GC tissue samples. According to the area under the ROC curve (AUC) values (AUC = 0.829 in the TCGA and 0.966 in the GSE54129 dataset), CDH6 expression was associated with high diagnostic efficacy. Patients with high CDH6 levels in their GC tissues had a higher T number (according to the TNM classification) and a worse prognosis than those with low CDH6 expression. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001).

CONCLUSION

CDH6 was upregulated in GC, and high CDH6 expression was indicative of a higher T number and a worse prognosis. Therefore, CDH6 represents a potentially independent molecular biomarker for the diagnostic and prognostic prediction of GC.

Molecular and cellular characterization of two patient-derived ductal carcinoma in situ (DCIS) cell lines, ETCC-006 and ETCC-010

BACKGROUND

Currently it is unclear how in situ breast cancer progresses to invasive disease; therefore, a better understanding of the events that occur during the transition to invasive carcinoma is warranted. Here we have conducted a detailed molecular and cellular characterization of two, patient-derived, ductal carcinoma in situ (DCIS) cell lines, ETCC-006 and ETCC-010.

METHODS

Human DCIS cell lines, ETCC-006 and ETCC-010, were compared against a panel of cell lines including the immortalized, breast epithelial cell line, MCF10A, breast cancer cell lines, MCF7 and MDA-MB-231, and another DCIS line, MCF10DCIS.com. Cell morphology, hormone and HER2/ERBB2 receptor status, cell proliferation, survival, migration, anchorage-independent growth, indicators of EMT, cell signalling pathways and cell cycle proteins were examined using immunostaining, immunoblots, and quantitative, reverse transcriptase PCR (qRT-PCR), along with clonogenic, wound-closure and soft agar assays. RNA sequencing (RNAseq) was used to provide a transcriptomic profile.

RESULTS

ETCC-006 and ETCC-010 cells displayed notable differences to another DCIS cell line, MCF10DCIS.com, in terms of morphology, steroid-receptor/HER status and markers of EMT. The ETCC cell lines lack ER/PR and HER, form colonies in clonogenic assays, have migratory capacity and are capable of anchorage-independent growth. Despite being isogenic, less than 30% of differentially expressed transcripts overlapped between the two lines, with enrichment in pathways involving receptor tyrosine kinases and DNA replication/cell cycle programs and in gene sets responsible for extracellular matrix organisation and ion transport.

CONCLUSIONS

For the first time, we provide a molecular and cellular characterization of two, patient-derived DCIS cell lines, ETCC-006 and ETCC-010, facilitating future investigations into the molecular basis of DCIS to invasive ductal carcinoma transition.

Genome-Scale Analysis Identified NID2, SPARC, and MFAP2 as Prognosis Markers of Overall Survival in Gastric Cancer

BACKGROUND Gastric cancer is the most common gastrointestinal tumor, and the rates of recurrence and metastasis are high. Research results on molecular biomarkers used for prognosis of gastric cancer remain inconclusive. This study aimed to explore the gene expression module of gastric cancer and to determine potential prognostic biomarkers. MATERIAL AND METHODS Three microarray datasets (GSE13911, GSE79973, and GSE29272) from Gene Expression Omnibus (GEO), including 206 pairs of gastric tumors and adjacent normal samples, were used for analysis of differentially expressed genes (DEGs). The 3 microarray datasets yielded 144 genes associated with the progression and prognosis of gastric cancer. After this, a risk score model was developed for result validation using an independent dataset from The Cancer Genome Atlas. RESULTS The validation of the independent dataset showed significantly increased NID2, SPARC, and MFAP2 expression in gastric tumor tissues, which were associated with poor outcomes in gastric cancer patients. Moreover, the high risk score obtained was associated with poor overall survival (HR: 1.787; 1.069-2.986; P=0.027). Subgroup analyses revealed that these significant prognostic values were detected in patients aged <65.0 years, tumors in the antrum/distal colon, grade 3 tumors, or TNM-M0 stages of cancer. CONCLUSIONS The findings of this study show that NID2, SPARC, and MFAP2 are upregulated in gastric tumor tissues and are significantly associated with poor overall survival. Therefore, the predictive values of the risk score model employed for the prognosis of gastric cancer could be improved by using these 3 upregulated DEGs.

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

High expression of VCAN is an independent predictor of poor prognosis in gastric cancer

Objective

Methods

Results

Conclusion

ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer

BACKGROUND

Gastric cancer (GC) is among the most common forms of cancer affecting the digestive system. This study sought to identify hub genes regulating early GC (EGC) in order to explore their potential for early diagnosis and prognosis of patients.

METHODS

We utilized a publically available dataset from the Gene Expression Omnibus database (GSE55696). Differences between EGC and LGIN with respect to gene expression were compared using the limma software. Identified differentially expressed genes (DEGs) were subjected to gene ontology (GO) and pathway enrichment analyses with the DAVID application, and the STRING website and Cytoscape software were used to construct a protein-protein interaction (PPI) network incorporating these DEGs. This network was in turn used to identify hub genes among selected DEGs, which were analyzed with the Kaplan-Meier Plotter database. In addition, Western blotting, qRT-PCR, immunohistochemistry, and UALCAN were all employed to validate the relationship between the expression of these genes and GC patient prognosis.

RESULTS

A total of 482 DEGs were identified, with GO analyses indicating an increase in the expression of genes linked with the development of cancer. Pathway analyses also indicated that these genes play a role in certain cancer-related pathways. The PPI network highlighted four potential hub genes, of which only ICAM1 was linked to a poor GC patient prognosis. This link between ICAM1 and GC patient outcomes was confirmed via UALCAN, Western blotting, immunohistochemistry, and qRT-PCR.

CONCLUSION

ICAM1 may therefore modulate tumor progression in GC, thus potentially representing a valuable prognostic and diagnostic biomarker of EGC.

Identification and Analysis of Novel Biomarkers Involved in Chromophobe Renal Cell Carcinoma by Integrated Bioinformatics Analyses

In renal cell carcinoma, chromophobe renal cell carcinoma (ChRCC) is a distinct subtype, whose clinical manifestations often lack specificity, and the molecular mechanisms of ChRCC tumorigenesis remain generally vague. The target of this study was to discover novel biomarkers involved in ChRCC by integrated bioinformatics analyses. We found 2608 differentially expressed genes (DEGs), of which 1518 were upregulated and 1090 were downregulated. Gene ontology (GO) analysis of DEGs uncovered significant functional enrichment in three aspects: biological process (BP), molecular function (MF), and cellular component (CC). The results of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated DEGs were largely enriched in retinol metabolism, arachidonic acid metabolism, and pentose and glucuronate interconversions. Then, the protein–protein interactions (PPI) network was constructed and top three hub genes were identified by the Cytoscape plugin cytoHubba. Through calculating the degree, betweenness centrality, and Stress of mRNAs, CENPA was upregulated and KNG1 and AGT were downregulated. A survival assay performed according to Oncomine data showed only CENPA high expression exhibited a worse prognosis. This study identified crucial genes and pathways for the progress of ChRCC, and CENPA might be a novel biomarker for diagnosis, treatment, and prognosis of ChRCC.