Integrated whole genome microarray analysis and immunohistochemical assay identifies COL11A1, GJB2 and CTRL as predictive biomarkers for pancreatic cancer

Sun, Defeng; Jin, Haoyi; Zhang, Jun; Tan, Xiaodong

doi:10.1186/s12935-018-0669-x

Cited by 29 publications

(21 citation statements)

References 27 publications

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“…Besides, many studies have also demonstrated that the three genes may serve as promising biomarkers for cancer. For example, GJB2 promoted early stage breast cancer progression by regulating cancer stemness [28]; Zhu et al suggested that GJB2 was a prognostic marker in patients with pancreatic cancer by genome-scale analysis [29]; the group of Sun D found that GJB2 was identified as predictive biomarkers for pancreatic cancer by integrated whole genome microarray analysis and immunohistochemical assay [30]; Masuda et al indicated that overexpressed S100A2 was a prognostic marker for patients with stage II and III colorectal cancer [31]; Kwon et al showed that S100A2 was significant for progression of prostate adenocarcinoma [32]. Regrading to SPOCK2, the group of Ren F confirmed that SPOCK2 contributed to the progression of endometrial cancer by modulating the biological behavior of cancer cells [33].…”

Section: Discussionmentioning

confidence: 99%

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Lou

Ding

Zhong

et al. 2019

Aging

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Objective: Ovarian cancer is one of the most common and lethal cancer types in women. The molecular mechanism of ovarian cancer progression is still unclear.Results: Here, we first reported that expression levels of three genes, GJB2, S100A2 and SPOCK2, were significantly higher in advanced stage than that in early stage of ovarian cancer, and upregulation of them indicated poor prognosis of patients with ovarian cancer. Subsequently, 8, 6 and 20 miRNAs were predicted to target GJB2, S100A2 and SPOCK2, respectively. Among these miRNA-mRNA pairs, hsa-miR-363-3p-SPOCK2 axis was the most potential in suppressing progression of ovarian cancer. Mechanistically, we found that hsa-miR-363-3p-SPOCK2 axis was involved in regulation of actin cytoskeleton. Moreover, 6 pseudogenes and 8 lncRNAs were identified to potentially inhibit hsa-miR-363-3p-SPOCK2 axis in ovarian cancer.Conclusions: Collectively, we elucidate a regulatory role of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway in progression of ovarian cancer, which may provide effective therapeutic approaches and promising prognostic biomarkers for ovarian cancer.Materials and methods: Differentially expressed genes (DEGs) in ovarian cancer were first screened using GSE12470, after which DEGs expression were validated using GEPIA. Kaplan-Meier analysis was employed to assess the prognostic values. Potential miRNAs were predicted by seven target prediction databases, and upstream lncRNAs and pseudogenes of hsa-miR-363-3p were forecasted through miRNet or starBase. UALCAN and starBase were used to obtain the co-expressed genes of SPOCK. Enrichment analysis for these co-expressed genes was performed by Enrichr.

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Section: Discussionmentioning

confidence: 99%

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Lou

Ding

Zhong

et al. 2019

Aging

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“…Therefore, uncovering the mechanism of hypoxia-regulated responses in PC cells will aid in its treatment. PC; they had the potential to predict poor outcomes (Sun, Jin, Zhang, & Tan, 2018). Based on the gene expression profile of PC tissues in the TCGA database, YAP1 was identified as an independent prognostic marker and was associated with extracellular matrix remodelling (Zhou et al, 2020).…”

Section: Restoration Of Yeats2 Blocks the Inhibitory Effects Of Hifmentioning

confidence: 99%

YEATS2 is a target of HIF1α and promotes pancreatic cancer cell proliferation and migration

Zeng

Lei

et al. 2020

Journal Cellular Physiology

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Hypoxia is involved in the development of pancreatic cancer (PC). The responses of hypoxia-associated genes and their regulated mechanisms are largely unknown. In this study, through bioinformatic analysis and quantitative real-time polymerase chain reaction, the YEATS domain containing 2 (YEATS2) was determined to be a key hypoxia-associated gene. It was increased in PC cells under hypoxia, upregulated in PC tissues, and predicted poor outcome. YEATS2 inhibition decreased the proliferation and migration of PC cells under both normoxia and hypoxia in vitro as well as proliferation and metastasis in vivo. We found that hypoxia-inducible factor 1α (HIF1α) regulated the expression of YEATS2 via binding to the hypoxia response element (HRE) of YEATS2 and coexpressed with YEATS2 in PC tissues. Overexpression of YEATS2 blocked the inhibitory effects of HIF1α silence on PC cell proliferation and migration under hypoxia. Collectively, our study revealed that YEATS2 is a target gene of HIF1α and promotes PC development under hypoxia.

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“…Recently, it has been reported that several predicted models for risk estimation, such as S100P , ERO1LB , SULF1 , ITGA2 , GPRC5A, ACTN4, LMO2, p16INK4a and CLPS (Watanabe et al, 2015; Nakata et al, 2009; Gerdes et al, 2002; Lyu et al, 2018; Zhang et al, 2013; Ji et al, 2014; Zhu et al, 2017; Li et al, 2018; Liu et al, 2018). A study recently identified three genes COL11A1 , GJB2 and CTRL as prognostic biomarkers for PC by using integrated whole genome microarray analysis and immunohistochemical assay (Sun et al, 2018). Most existing prognosis models of PC involve only one gene or mRNA, which have their limitations.…”

Section: Discussionmentioning

confidence: 99%

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

Zhou

Chen

Zhang

et al. 2019

Mol Med

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BackgroundThe hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.MethodsRobust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.ResultsBased on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = − 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).ConclusionIntegrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

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Integrated whole genome microarray analysis and immunohistochemical assay identifies COL11A1, GJB2 and CTRL as predictive biomarkers for pancreatic cancer

Cited by 29 publications

References 27 publications

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

YEATS2 is a target of HIF1α and promotes pancreatic cancer cell proliferation and migration

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

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