Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Yan, Haimeng; Zheng, Gaofeng; Qu, Jianwei; Yang, Liu; Huang, Xi; Zhang, Enfan; Zhang, Cai

doi:10.1002/jcp.28947

Cited by 51 publications

(46 citation statements)

References 67 publications

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“…The logFC values of DEGs reflects the level of up or downregulation of the gene. The higher the logFC values in the upregulated DEGs, the greater the degree of upregulation of the gene, and the lower the logFC values in the downregulated DEGs, the greater the degree of downregulation (41)(42)(43). Thus it was hypothesized that the DEGs with the highest and lowest logFC values would be the genes most closely associated with disease mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential key genes in gastric cancer using bioinformatics analysis

Wang

Zhao

et al. 2020

biom rep

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Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

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Section: Discussionmentioning

confidence: 99%

Identification of potential key genes in gastric cancer using bioinformatics analysis

Wang

Zhao

et al. 2020

biom rep

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“…The expression levels of LncRNAs were determined using quantitative real-time polymerase chain reaction (qRT-PCR). Seven types of lncRNAs were recognized as tumor promoters [10,11,13,14,[21][22][23][24][25] and six were tumor suppressors [7,9,[26][27][28][29] in CRC patients. The associations between lncRNAs and clinicopathologic parameters in CRC patients are shown in Table 1.…”

Section: Study Parameters and Study Qualitymentioning

confidence: 99%

Clinical diagnostic value of long non-coding RNAs in Colorectal Cancer: A systematic review and meta-analysis

Bi¹,

Zhang²,

Fan³

et al. 2020

J. Cancer

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Background: Histopathological diagnosis remains the gold standard for the diagnosis of cancer, including colorectal cancer, but it is infeasible when tumor tissue is not available. With the recognition of long non-coding RNAs (lncRNAs), the expression of lncRNAs in serum or tissue samples has been reported as a diagnosis method for some cancers, however, the diagnostic value of lncRNAs for colorectal cancer remains unclear. Methods: A systematic review and meta-analysis were conducted. Eligible studies were identified through a comprehensive literature search in PubMed, PubMed Central, Web of Science, Embase, and Cochrane Library (up to May 05, 2020) according to the selection criteria. Meta-DiSc, Review Manager and STATA were used to analyze the association between lncRNAs expression and the diagnosis of colorectal cancer. Results: Fifteen studies that analyzed the expression of 15 lncRNAs in 1434 CRC patients were included. The summary area under the curve (AUC) of lncRNA for the diagnosis efficacy between patients with and without CRC was estimated to be 0.8629, corresponding to a weighted sensitivity of 0.75 (95% CI: 0.72-0.77), specificity of 0.80 (95%CI: 0.78-0.82). Subgroup analysis illustrated that the AUC of blood-based detection of lncRNA showed 0.8820, pooled DOR: 18.57, while tissue-based analysis showed 0.8203, pooled DOR: 10.47. Blood-based tests were then divided into two categories, plasma-based and serum-based lncRNA testing. Results revealed that the AUC of serum-based detection was 0.9077, pooled DOR: 26.64, and plasma-based detection was 0.5000, pooled DOR: 11.80. Conclusions: This meta-analysis indicates that the aberrantly expressed lncRNAs might serve as potential diagnostic biomarkers for CRC patients and blood-based lncRNA analysis is of higher diagnostic accuracy than tissue-based testing. Moreover, serum-based lncRNA testing achieved higher diagnostic efficacy than plasma-based analysis.

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“…The selection criteria of DEGs were based on published literatures. [16][17][18] Cao's study used |log FC|≥1 and the adj. P < 0.05 as the selection criteria of DEGs and DE miRNAs selection.…”

Section: Data Processingmentioning

confidence: 99%

“…17 The screening conditions for the DEGs were absolute value of p < 0.05 and |log FC| >1 in Yan's study. 18 Therefore, compared with the normal thyroid specimens, the GEO2R was conducted to screen DEGs in each chip under the condition of |log FC| > l, adjusted P-values (adj. P) <0.05.…”

Section: Data Processingmentioning

confidence: 99%

<p>Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis</p>

Wang

Guo

et al. 2020

CMAR

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The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC. Methods: Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P-values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cutoff criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan-Meier plotter from those hub genes and validated by RT-qPCR. Results: A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan-Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3-1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells. Conclusion: Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

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Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Cited by 51 publications

References 67 publications

Identification of potential key genes in gastric cancer using bioinformatics analysis

Identification of potential key genes in gastric cancer using bioinformatics analysis

Clinical diagnostic value of long non-coding RNAs in Colorectal Cancer: A systematic review and meta-analysis

<p>Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis</p>

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