Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

Zhou, Zhou; Cheng, Yi-An; Jiang, Yinan; Shi, Liu; Zhang, Meng; Liu, Jing; Zhao, Qiu

doi:10.7150/ijbs.22619

Cited by 134 publications

(139 citation statements)

References 60 publications

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“…Annotated gene sets c2.cp.kegg.v6.0.symbols.gmt was chosen as the reference gene sets, including terms with FDR less than 0.05. 24…”

Section: Gene Set Enrichment Analysismentioning

confidence: 99%

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Chen

et al. 2019

J of Cellular Biochemistry

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Ovarian carcinoma (OC) is one of the most common malignant tumors in female genitals. In recent years, the therapeutic effect of OC has been significantly improved through the application of effective chemotherapy regimen. However, the 5‐year survival rate is also lower than 30% due to high rate of relapse. So, it is needed to screen reliable predictive and prognostic markers of OC. Ovarian cancer gene expression data and corresponding clinical data used were downloaded from Gene Expression Omnibus database. Weighted gene expression network analysis (WGCNA) and Cox proportional hazards regression (PHR) were used to screen Pathological Grade and Prognosis‐associated long noncoding RNA (lncRNA). Kaplan‐Meier analysis and receiver operating characteristic curves analysis were performed to evaluate the predictive ability of the selected lncRNA. Gene Ontology (GO) enrichment and Gene Set Enrichment Analysis (GSEA) enrichment analysis methods were used to explore the possible mechanisms of the selected lncRNA affecting the development of OC. Five reliably lncRNAs (LINC00664, LINC00667, LINC01139, LINC01419, and LOC286437) was identified through a series of bioinformatics methods. In testing cohorts, we found that the five lncRNAs in predicting the risk of OC recurrence is robustness, and multivariate Cox PHR analysis indicate that the five lncRNAs is an independent risk factor for OC recurrence. Moreover, GO and GSEA enrichment analysis showed that the five lncRNAs are involved in multiple ovarian cancer occurrence mechanism. In summary, all these findings indicated that the five lncRNAs can effectively predict the risk of recurrence of ovarian cancer.

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“…Annotated gene sets c2.cp.kegg.v6.0.symbols.gmt was chosen as the reference gene sets, including terms with FDR less than 0.05. 24…”

Section: Gene Set Enrichment Analysismentioning

confidence: 99%

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Chen

et al. 2019

J of Cellular Biochemistry

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“…Then, all genes in the hub module were analyzed to construct a protein‐protein interaction (PPI) network, and genes with a connectivity degree of ≥ 8 also were defined as hub genes. Genes in both the coexpression network and the PPI network were considered as “real” hub genes for further analysis …”

Section: Methodsmentioning

confidence: 99%

“…Samples from the GSE51373 test set were divided into two groups according to chemotherapy resistance or sensitivity to identify the potential function of KLF6 using gene set enrichment analysis (GSEA) (http://software.broadinstitute.org/gsea/index.jsp, RRID: SCR_003199). Annotated gene sets c2.cp.kegg.v6.0.symbols.gmt was chosen as the reference gene sets, including terms with FDR < 0.05 …”

Section: Methodsmentioning

confidence: 99%

“…Genes in both the coexpression network and the PPI network were considered as "real" hub genes for further analysis. 10,19…”

Section: Identification Of Clinically Significant Modules and Functmentioning

confidence: 99%

“…The method considers common expression patterns between two genes and the overlap of adjacent gene expression patterns . WGCNA has been used to screen hub genes associated with clinical traits in a number of cancer types, such as pancreatic carcinoma and clear cell renal cell carcinoma . To identify specific biomarkers for chemotherapeutic resistance in OC, we performed WGCNA to screen the relevant hub genes.…”

Section: Introductionmentioning

confidence: 99%

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Coexpression network analysis identified Krüppel‐like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

Chen

et al. 2018

J of Cellular Biochemistry

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Although most patients with ovarian cancer (OC) are initially sensitive to paclitaxel/carboplatin combination chemotherapy, eventually they develop resistance to chemotherapy drugs and experience disease relapse. OC is the most lethal gynecological malignancy, and the five-year survival rate is extremely low. Thus, research on specific biomarkers and potential targets for chemotherapy-resistant patients with OC is needed. In our study, genes in the top 10% of variance in data set GSE30161 from chemoresistant and chemosensitive OC tissues were determined to conduct a weighted gene coexpression network analysis (WGCNA). The magenta module was most strongly related to OC chemoresponse. Gene ontology enrichment analysis indicated that the function of the magenta module primarily focused on transcription regulation, cell cycle control, and apoptosis modulation. Integration of the WGCN with the protein-protein interaction network identified five candidate genes. These five genes were verified using the GSE51373 test set, and Krüppel-like factor 6 ( KLF6) was identified as tightly linked to OC chemosensitivity. The receiver operating characteristic (ROC) curve showed that KLF6 differentiated chemoresistant from chemosensitive OC tissues. The Kaplan-Meier online database indicated that high KLF6 expression was associated with poor OC prognosis. Gene set enrichment analysis determined that the KLF6 mechanism was potentially associated with cell cycle, mTOR, and DNA-damage repair signaling pathways. In conclusion, KLF6 was identified in association with OC chemoresistance, and the mechanism of KLF6-mediated chemoresistance may involve the cell cycle, mTOR, and DNA-damage repair signaling pathways.

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LRRK2 Gly2019Ser Mutation Promotes ER Stress via Interacting with THBS1/TGF‐β1 in Parkinson's Disease

Yao,

Lu,

Koc

et al. 2023

Advanced Science

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The gene mutations of LRRK2, which encodes leucine‐rich repeat kinase 2 (LRRK2), are associated with one of the most prevalent monogenic forms of Parkinson's disease (PD). However, the potential effectors of the Gly2019Ser (G2019S) mutation remain unknown. In this study, the authors investigate the effects of LRRK2 G2019S on endoplasmic reticulum (ER) stress in induced pluripotent stem cell (iPSC)‐induced dopamine neurons and explore potential therapeutic targets in mice model. These findings demonstrate that LRRK2 G2019S significantly promotes ER stress in neurons and mice. Interestingly, inhibiting LRRK2 activity can ameliorate ER stress induced by the mutation. Moreover, LRRK2 mutation can induce ER stress by directly interacting with thrombospondin‐1/transforming growth factor beta1 (THBS1/TGF‐β1). Inhibition of LRRK2 kinase activity can effectively suppress ER stress and the expression of THBS1/TGF‐β1. Knocking down THBS1 can rescue ER stress by interacting with TGF‐β1 and behavior burden caused by the LRRK2 mutation, while suppression of TGF‐β1 has a similar effect. Overall, it is demonstrated that the LRRK2 mutation promotes ER stress by directly interacting with THBS1/TGF‐β1, leading to neural death in PD. These findings provide valuable insights into the pathogenesis of PD, highlighting potential diagnostic markers and therapeutic targets.

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Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

Cited by 134 publications

References 60 publications

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Coexpression network analysis identified Krüppel‐like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

LRRK2 Gly2019Ser Mutation Promotes ER Stress via Interacting with THBS1/TGF‐β1 in Parkinson's Disease

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