Integrating high‐throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis

Wang, Ping; Li, Weidong; Zhai, Bo; Jiang, Xian; Jiang, Hongchi; Zhang, Chunlong; Sun, Xueying

doi:10.1002/jcb.29576

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…In this study, we found that 4 miRNAs are significantly related to the prognosis of PAAD, and they were verified by GEO database. Among all the verified samples, high expression of miR-934 was consistently found to be significantly associated with poor prognosis of pancreatic cancer and several studies have shown that miR-934 promotes cancer metastasis (41)(42)(43)(44)(45)(46)(47)(48). Related, this miRNA has not been reported in PAAD, but an article reported that miR-934 is significantly up-regulated in pancreatic ductal adenocarcinoma (PDAC) and is included in the 9-miRNA panel to predict poor survival (45).…”

Section: Discussionmentioning

confidence: 99%

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

et al. 2022

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Pancreatic adenocarcinomas (PAADs) often remain undiagnosed until later stages, limiting treatment options and leading to poor survival. The lack of robust biomarkers complicates PAAD prognosis, and patient risk stratification remains a major challenge. To address this issue, we established a panel constructed by four miRNAs (miR-4444-2, miR-934, miR-1301 and miR-3655) based on The Cancer Genome Atlas (TCGA) and Human Cancer Metastasis Database (HCMDB) to predicted the prognosis of PAAD patients. Then, a risk prediction model of these four miRNAs was constructed by using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) regression analysis. This model stratified TCGA PAAD cohort into the low-risk and high-risk groups based on the panel-based risk score, which was significantly associated with 1-, 2-, 3-year OS (AUC=0.836, AUC=0.844, AUC=0.952, respectively). The nomogram was then established with a robust performance signature for predicting prognosis compared to clinical characteristics of pancreatic cancer (PC) patients, including age, gender and clinical stage. Moreover, two GSE data were validated the expressions of 4 miRNAs with prognosis/survival outcome in PC. In the external clinical sample validation, the high-risk group with the upregulated expressions of miR-934/miR-4444-2 and downregulated expressions of miR-1301/miR-3655 were indicated a poor prognosis. Furthermore, the cell counting kit-8 (CCK-8) assay, clone formation, transwell and wound healing assay also confirmed the promoting effect of miR-934/miR-4444-2 and the inhibiting effect of miR-1301/miR-3655 in PC cell proliferation and migration. Taken together, we identified a new 4-miRNA risk stratification model could be used in predicting prognosis in PAAD.

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Section: Discussionmentioning

confidence: 99%

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

et al. 2022

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“…Based on the global combined network which contained both PPI interaction and co‐expression relations, we further performed a global risk impact analysis to optimize mRNAs by using the random walk algorithm. The random walk algorithm was developed and utilized for multiple types of disease mechanism analysis and displayed more advantages in risk or prognostic genes identification 19,22,23 on the basis of the global network. Based on the reconstructed network mentioned above, the functional genes from HR/FA pathways were regarded as seed nodes.…”

Section: Methodsmentioning

confidence: 99%

The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis

Xing

Zhang

et al. 2020

J Cellular Molecular Medi

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Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination and Fanconi anaemia pathways. Associations between these two functional genes were explored in the study, and we performed a random walk algorithm based on reconstructed gene‐gene network, including protein‐protein interaction and co‐expression relations. Following the random walk, all genes were ranked and GSEA analysis showed that the biological functions focused primarily on autophagy, histone modification and gluconeogenesis. Based on three types of seed nodes, the top two genes were utilized as examples. We selected a total of six candidate genes (FANCA, FANCG, POLD1, KDM1A, BLM and BRCA1) for subsequent verification. The validation results of the six candidate genes have significance in three independent ovarian cancer data sets with platinum‐resistant and platinum‐sensitive information. To explore the correlation between biomarkers and clinical prognostic factors, we performed differential analysis and multivariate clinical subgroup analysis for six candidate genes at both mRNA and protein levels. And each of the six candidate genes and their neighbouring genes with a mutation rate greater than 10% were also analysed by network construction and functional enrichment analysis. In the meanwhile, the survival analysis for platinum‐treated patients was performed in the current study. Finally, the RT‐qPCR assay was used to determine the performance of candidate genes in ovarian cancer platinum response. Taken together, this research demonstrated that comprehensive bioinformatics methods could help to understand the molecular mechanism of platinum response and provide new strategies for overcoming platinum resistance in ovarian cancer treatment.

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“…Based on the obtained DEgenes and PPI network, we further performed a global risk impact analysis (named random walk) to optimize and identify mRNAs. The random walk algorithm was developed for candidate factor analysis; it displays advantages in identifying signature genes on the basis of global network information (Kohler et al, 2008;Zhang et al, 2014;Wang et al, 2020). The related analysis was performed using R RWOAG package with default parameters (r = 0.6) and equal weight for each DEgene as seed node.…”

Section: Differential Expression and Random Walk Analysesmentioning

confidence: 99%

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

Xing

Tian

et al. 2021

Front. Cell Dev. Biol.

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Ovarian cancer is the most frequent cause of death among gynecologic malignancies. A total of 80% of patients who have completed platinum-based chemotherapy suffer from relapse and develop resistance within 2 years. In the present study, we obtained patients' complete platinum (cisplatin and carboplatin) medication information from The Cancer Genome Atlas database and then divided them into two categories: resistance and sensitivity. Difference analysis was performed to screen differentially expressed genes (DEgenes) related to platinum response. Subsequently, we annotated DEgenes into the protein–protein interaction network as seed nodes and analyzed them by random walk. Finally, second-ranking protease serine 1 gene (PRSS1) was selected as a candidate gene for verification analysis. PRSS1's expression pattern was continuously studied in Oncomine and cBio Cancer Genomic Portal databases, revealing the key roles of PRSS1 in ovarian cancer formation. Hereafter, we conducted in-depth explorations on PRSS1's platinum response to ovarian cancer through tissue and cytological experiments. Quantitative real-time polymerase chain reaction and Western blot assay results indicated that PRSS1 expression levels in platinum-resistant samples (tissue/cell) were significantly higher than in samples sensitive to platinum. By cell transfection assay, we observed that knockdown of PRSS1 reduced the resistance of ovarian cancer cells to cisplatin. Meanwhile, overexpression of PRSS1 increased the resistance to cisplatin. In conclusion, we identified a novel risk gene PRSS1 related to ovarian cancer platinum response and confirmed its key roles using multiple levels of low-throughput experiments, revealing a new treatment strategy based on a novel target factor for overcoming cisplatin resistance in ovarian cancer.

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Integrating high‐throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis

Cited by 8 publications

References 27 publications

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

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