Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach

Fang, Enhao; Zhang, Xiuqing; Wang, Qi; Wang, Daoming

doi:10.3233/cbm-170362

Cited by 24 publications

(20 citation statements)

References 40 publications

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“…As marked in bold fonts in Table 6, there were some overlapped findings between our studies and other studies: our identified hub genes FOXA1, CDH1, and EPCAM were seen in 2 (Endo et al 2009; Tan et al 2019), 1 (Fang et al 2017), and 1 (Lu 2019) other studies, respectively. Next, we further validated our findings using data from TCGA.…”

Section: Validation Of Hub Genes Expression In Multiple Databasessupporting

confidence: 50%

“…As shown in Table 6, the literature search in Pubmed/GEO regarding gene expression profiles of PCa versus NP tissues yielded 10 relevant studies, 5 (Chen et al 2012;Endo et al 2009;Fan et al 2018;Fang et al 2017;He et al 2018) of which were based on a single GEO dataset whereas the other 5 studies (Lu 2019;Song et al 2019b;Tan et al 2019;Tong et al 2019;Zhao et al 2017) were integrated bioinformatic analyses based on multiple GEO datasets. The hub genes reported by the 10 eligible studies were extracted and compared with those identified in the present study.…”

Section: Validation Of Hub Genes Expression In Multiple Databasesmentioning

confidence: 99%

“…With the development of high-throughput sequencing technology and bioinformatic analysis methods, the Gene Expression Omnibus (GEO) online public database has been widely utilized to screen out differentially expressed genes (DEGs), to study molecular signals and their relations, and to aid in constructing gene regulatory networks (Clough & Barrett 2016). Up to now, by either analysis of a single dataset or integrated analysis of multiple datasets in GEO, several studies have dug out genes that exert important influence on the occurrence and progression of PCa, such as CDH1 (Fang et al 2017), CDCA8 (Zhao et al 2017), RPS21 (Fan et al 2018), PIK3R1 (He et al 2018), EPCAM (Lu 2019), LMNB1 (Song et al 2019b), IGF2 (Tan et al 2019), and IKZF1 (Tong et al 2019). However, the key genes detected by the above studies are largely different from each other and had little in common, and such discrepancy could be attributed to the fact that PCa is PeerJ reviewing PDF | (2019:06:38928:2:0:NEW 4 Sep 2019)…”

Section: Introductionmentioning

confidence: 99%

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Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

2019

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Background:Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method. Methods: Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The UCSC Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.Results: Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in 'oxidation-reduction process' and 'positive regulation of transcription from RNA polymerase II promoter'; KEGG pathway analysis enriched mostly in 'metabolic pathways' and 'protein digestion and absorption'. KLK3, CDH1, KLK2, FOXA1, and EPCAM were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant GEO datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.Conclusions: This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

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Section: Validation Of Hub Genes Expression In Multiple Databasessupporting

confidence: 50%

Section: Validation Of Hub Genes Expression In Multiple Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

2019

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“…These findings indicated that CD81 functions as an oncogene in prostate cancer and may be a potential prognostic biomarker for human prostate cancer. In recent years, an increasing number of studies have focused on the development of accurate molecular biomarkers for better detection, diagnosis, prognosis, and treatment (18)(19)(20). For instance, forkhead transcription factor (FoxM1) functions as an oncogene in the initiation, development, and progression of cancer, and its neoplastic functions can be used as a strong biomarker for the diagnosis and treatment of cancer (21).…”

Section: Discussionmentioning

confidence: 99%

Increased expression of CD81 is associated with poor prognosis of prostate cancer and increases the progression of prostate cancer cells in�vitro

Qian

Yang

2019

Exp Ther Med

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CD81, a member of the tetraspanin family, has been revealed to be upregulated and associated with prognosis in several types of cancer; however, this relationship has not been explored in prostate cancer. The present study aimed to investigate the prognostic significance and functional role of CD81 in prostate cancer. The expression of CD81 in prostate cancer tissues and cell lines was evaluated using qRT-PCR analysis. Kaplan-Meier survival analysis and Cox regression analysis were conducted to explore the prognostic significance of CD81. Cell experiments were used to explore the effects of CD81 on cell proliferation, migration, and invasion in prostate cell lines in vitro. The expression of CD81 was increased in both prostate cancer tissues and cell lines. Upregulation of CD81 was significantly associated with lymph node metastasis and TNM stage. Moreover, patients with high CD81 levels had poorer overall survival than those with lower levels. Additionally, tumor cell proliferation, migration, and invasion were inhibited by knockdown of CD81. The present results indicated that CD81 plays an oncogenic role in prostate cancer. Overexpression of CD81 may serve as a prognostic biomarker and therapeutic target and is involved in the progression of prostate cancer.

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“…Many comparative analyses of the DEGs have been carried out on cancers in recent years. [7][8][9] However, none of the aforementioned studies has been completely investigated in AL amyloidosis.…”

Section: Introductionmentioning

confidence: 99%

<p>Identification of Candidate Genes and Therapeutic Agents for Light Chain Amyloidosis Based on Bioinformatics Approach</p>

Bai¹,

Hong-hua²,

Bai

2020

PGPM

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Systemic amyloid light chain (AL) amyloidosis is a rare plasma cell disease. However, the regulatory mechanisms of AL amyloidosis have not been thoroughly uncovered, identification of candidate genes and therapeutic agents for this disease is crucial to provide novel insights into exploring the regulatory mechanisms underlying AL amyloidosis. Methods: The gene expression profile of GSE73040, including 9 specimens from AL amyloidosis patients and 5 specimens from normal control, was downloaded from GEO datasets. Differentially expressed genes (DEGs) were sorted with regard to AL amyloidosis versus normal control group using Limma package. The gene enrichment analyses including GO and KEGG pathway were performed using DAVID website subsequently. Furthermore, the protein-protein interaction (PPI) network for DEGs was constructed by Cytoscape software and STRING database. DEGs were mapped to the connectivity map datasets to identify potential molecular agents of AL amyloidosis. Results: A total of 1464 DEGs (727 up-regulated, 737 down-regulated) were identified in AL amyloidosis samples versus control samples, these dysregulated genes were associated with the dysfunction of ribosome biogenesis and immune response. PPI network and module analysis uncovered that several crucial genes were defined as candidate genes, including ITGAM, ITGB2, ITGAX, IMP3 and FBL. More importantly, we identified the small molecular agents (AT-9283, Ritonavir and PKC beta-inhibitor) as the potential drugs for AL amyloidosis. Conclusion: Using bioinformatics approach, we have identified candidate genes and pathways in AL amyloidosis, which can extend our understanding of the cause and molecular mechanisms, and these crucial genes and pathways could act as biomarkers and therapeutic targets for AL amyloidosis.

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Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach

Abstract: The BMP2, PPARG and PRKAR2B genes may therefore be potential biomarkers in the treatment of PCa. Additionally, the small molecular agent phenoxybenzamine may be a potential drug for PCa.

Cited by 24 publications

References 40 publications

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

Increased expression of CD81 is associated with poor prognosis of prostate cancer and increases the progression of prostate cancer cells in�vitro

<p>Identification of Candidate Genes and Therapeutic Agents for Light Chain Amyloidosis Based on Bioinformatics Approach</p>

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